Вакансии

Location: Bulgaria; Croatia; Hungary; Lithuania; North Macedonia; Poland; Slovakia Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started. Join us as we build the future of autonomous infrastructure. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland,Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine. What you'll be solving Every dollar Cast AI saves a customer has to be provable, every action taken on their infrastructure has to be attributable to someone authorized to take it, and every price used to make that case has to be current across every cloud, region, and availability zone. This is the trust and accounting layer underneath the whole platform: get it wrong, and customers can't log in, can't trust the savings number, or get billed incorrectly - any one of which ends the relationship. This posting covers multiple teams working on different pieces of the same trust-and-accounting problem: authentication, authorization, and SSO for every user and workload. Billing, audit trails, notifications, and feature flags that keep the platform transparent for every customer. Real-time cloud pricing tracking across every region and availability zone, including Spot availability and discounts. And reporting that turns high-volume time-series data into fast, accurate answers about what the automation actually saved. Requirements Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. You've built or operated trust-critical systems at scale - authentication/authorization, billing, or financial/accounting-adjacent data, where being wrong has real consequences. Good understanding of API design best practices - versioning, backward compatibility, auth, idempotency, async patterns, and similar. Strong debugging, optimization, and performance-tuning skills – including query profiling, index design, and database performance tuning beyond ORM usage. Experience operating and optimizing systems under high load - you know how to find and fix the bottleneck, not just add more resources. Hands-on experience with Kubernetes - you understand the core concepts (pods, services, deployments) and have deployed or debugged something on a cluster. Experience with AI coding agents and agentic development workflows. You've personally driven a complex project end-to-end. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Participate in on-call rotation for teams that require it. Nice to haves Multi-tenant identity system design - authentication, authorization, SSO. Enterprise identity protocols - SAML, OIDC, and SCIM for enterprise account provisioning and sync. Billing, audit-trail, notification, or feature-flag systems in production. Experience with cloud provider pricing/billing APIs across AWS, GCP, or Azure, or tracking Spot instance availability. High-cardinality time-series ingestion and querying at scale. Understanding of Kubernetes internals. CI/CD and DevOps practices experience. Observability tooling (Prometheus, Grafana, OpenTelemetry) in production. Responsibilities Design, build, and operate backend services in Go that other teams and customers depend on being correct. Own features end-to-end: from design through implementation, testing, and production rollout. Debug complex production issues involving security-, money-, or trust-critical flows. Collaborate with product and other engineering teams to solve problems that don't have textbook solutions. Contribute to product direction and backlog - shape what gets built next, not just how it's built. Tools we use daily Programming Language: Go Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Infrastructure as Code: Terraform Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Prometheus, Grafana, Loki, Tempo CI/CD & GitOps: GitLab CI with ArgoCD What’s in it for you? Competitive salary (€6,500 – €9,000 gross, depending on the level of experience) plus separate compensation for on-call participation (passive coverage and active interventions) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. You'll be expected to travel to Vilnius for onboarding - travel costs are covered by the company. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Bulgaria; Croatia; Estonia; Greece; Hungary; Latvia; Lithuania; Poland; Romania; Slovakia; Slovenia; Ukraine Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started. Join us as we build the future of autonomous infrastructure. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine. About the role A team ships a new service, and getting it from a laptop to production - built, tested, deployed, and visible when something goes wrong - shouldn't require that team to become infrastructure experts. It should be a paved road: a CI/CD pipeline that just works, observability that's there by default, and standard ways of doing things that are the obvious best choice, because the alternative is every team reinventing the same solutions on its own. The Platform team builds and maintains that road, and the work is internal - the users are Cast AI's own engineers. The team is a multiplier for the engineering organization: whatever it builds is built once and serves every team, and the time product teams would otherwise spend on infrastructure goes back into developing their products. What the team owns is whether the tools engineers use to build, deploy, and observe their services are good enough that reliability is actually achievable, and with hundreds of engineers and dozens of services leaning on that at once, the platform has to be fast, correct, and boring in the best sense. Requirements: Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. Experience in platform engineering - you've built and operated (not just consumed) most, but not necessarily all of: CI/CD and GitOps systems (GitLab CI, Argo CD, Kargo), infrastructure as code (Terraform), container orchestration (Kubernetes, Helm), observability and monitoring tooling (VictoriaMetrics, Grafana, Loki, Tempo), and cloud platforms (AWS, GCP, or Azure). Experience with AI coding agents and agentic development workflows. You've personally driven a complex project end-to-end. Strong debugging, optimization, and performance-tuning skills. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Participate in on-call rotation for teams that require it. Nice to have: Kubernetes operators, Helm chart authoring, admission webhooks. Internal developer platform (IDP) design - golden paths, self-service tooling, service catalogs. Local development environment tooling (devcontainers, Tilt, Skaffold, or similar). Kubernetes or cloud-native OSS contributions. Responsibilities: Design, build, and operate the CI/CD pipelines every engineering team ships through. Build and maintain the observability and monitoring tooling other teams use to debug their own systems in production. Build local development environment tooling that lets engineers reproduce production-like conditions on their laptop. Reduce friction for every other team: identify where engineers lose time to infrastructure and fix it at the root, not with a workaround. Debug complex issues that span CI/CD, observability tooling, Kubernetes, and cloud infrastructure. Contribute to the platform roadmap and backlog - shape what gets built next for the engineers who depend on you. Tools we use daily: Programming Language: Go Cloud & Orchestration: Kubernetes, GCP, AWS, Azure Infrastructure as Code: Terraform Databases & Storage: ClickHouse, Cloud Object Storage Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Grafana, VictoriaMetrics, VictoriaLogs, VictoriaTraces, Loki, Tempo CI/CD & GitOps: GitLab CI, ArgoCD, Kargo What’s in it for you? Competitive salary (€6,500 – €9,000 gross, depending on the level of experience) plus separate compensation for on-call participation (passive coverage and active interventions) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. You'll be expected to travel to Vilnius for onboarding - travel costs are covered by the company. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Russia; Perm; Moscow Workplace: remote ABOUT US Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. THE ROLE We are hiring a Team Lead, Web3 to lead a cross-functional team of frontend, backend, smart contract and QA engineers in XsollaZK, Xsolla's Web3 ecosystem on a zkSync-based L2. Your team builds: smart wallets built on account abstraction; SDKs that game studios and partners use to bring players on-chain; infrastructure backend services for tokenizing in-game items and currencies; ecosystem dApps: a DEX, an NFT marketplace and an auction. This is a management role. Most of your time goes to people and delivery: building the team, growing engineers, and making delivery predictable. The rest goes to technical leadership: architecture, reviews and hands-on code where it matters. Responsibilities WHAT YOU WILL OWN The team: hiring, onboarding, growth and performance of frontend, backend, smart contract and QA engineers Delivery: predictable commitments, a sustainable pace and the quality of what the team ships How the team works: processes, engineering standards and an AI-assisted workflow Relationships with Product, DevOps and other Xsolla product teams: priorities, dependencies and expectations The technical direction of the team's product area: on-chain / off-chain architecture and key technical decisions, justified in writing Security and reliability of what the team ships: signing flows, contracts that hold user assets, production operation and the team's on-call rotation WHAT YOU WILL DO People Set clear goals and expectations for each engineer Run regular 1:1s, performance reviews and career development conversations; give direct feedback and address underperformance Hire engineers across disciplines: define roles, run interviews and onboard new people Grow engineers through mentoring and build the team's next leaders Build a culture of technical excellence, collaboration and continuous learning Delivery Work with Product to shape requirements, estimate and plan work Manage scope, workload, risks and dependencies so the team delivers on its commitments Report progress, risks and blockers to stakeholders Lead the team through production incidents and post-mortems Continuously improve development processes and team efficiency Technical leadership Review architecture and critical code across backend, frontend and smart contracts, with a focus on edge cases specific to Xsolla products: smart wallets, tokenization of in-game items and currencies, marketplace and auction flows Lead key decisions on smart wallet integration (UserOperations, paymaster and gas sponsorship), session keys and the signing flow Identify systemic risks (key management, signature phishing, bundler and RPC dependencies) and make sure they are addressed before they become incidents Contribute hands-on to critical features when needed Set up AI tooling in the repositories (Claude Code, Codex, Cursor: rules, context, harness) and drive its adoption across the team REQUIRED Management experience 5+ years of engineering management experience Managing cross-functional teams that combine several disciplines (for example frontend, backend, smart contracts and QA) Teams you led shipped dApps to production and maintained them People leadership Hiring and building teams: defining roles, running interviews, onboarding Performance management: setting goals, giving feedback, running performance reviews, handling underperformance Growing engineers: career plans, mentoring Delivery and stakeholders Delivery planning: estimation, sprint planning, managing scope, risks and dependencies Working with Product and other teams on priorities and trade-offs; explaining technical trade-offs to non-technical stakeholders Leading a team through production incidents and post-mortems Balancing management with hands-on technical work Technical depth A strong technical background: hands-on engineering experience across the Web3 stack, deep enough to lead technical decisions and review work on each layer: Web3 libraries and wallets: viem and wagmi in production; how wallet signers work (EOAs, browser and embedded wallets, smart accounts, EIP-191, EIP-712, EIP-1193); smart wallets (UserOperations, paymasters, ERC-1271) Backend: Go and TypeScript (Node.js); processing on-chain events (idempotency, reorgs, retries); asynchronous messaging (Kafka, RabbitMQ or NATS ); PostgreSQL and Redis; REST and gRPC API design Frontend: React and TypeScript; web security fundamentals ( XSS , CSRF , CSP , safe handling of signatures and keys in the browser) Smart contracts: reading Solidity and the EVM (storage, gas, proxy upgrade patterns, access control); common vulnerability classes (reentrancy, front-running / MEV , oracle manipulation, signature replay) Operations: Docker, Kubernetes, CI/CD; observability (we use Datadog; experience with Prometheus, Grafana, ELK / Graylog or OpenTelemetry also counts) AI-assisted engineering Daily hands-on use of AI coding tools (Claude Code, Codex, Cursor) for development and review, including configuring them for a repository English Spoken English at B2 level or higher Nice to Have ZK Stack and zk -rollups: EraVM vs EVM , system contracts (ContractDeployer, NonceHolder), L1↔L2 messaging, bridging and finality Smart contract development in Solidity with Foundry Deeper account abstraction: ERC-4337 EntryPoint versions and validation rules (ERC-7562, ERC-6492), modular accounts (ERC-7579 / ERC-6900), session keys and delegated permissions (ERC-7710 / ERC-7715), EIP-7702 Hands-on experience with wallet and AA infrastructure technologies (Sequence, thirdweb, Privy, Dynamic, Turnkey, Pimlico, Alchemy, ZeroDev, Safe{Core}), with clear views on build versus buy Key management: HSM , MPC / threshold signing, social or guardian-based recovery DeFi ( DEX , AMM ) and NFT marketplaces (ERC-721, ERC-1155, auctions) go-ethereum and abigen Managing distributed or remote teams Gaming or fintech experience with high-load systems HOW WE WORK Decisions in writing. Significant technical decisions are written up as an RFC or ADR that lays out the options and trade-offs. We discuss the document, not a blank whiteboard. AI is part of everyday engineering. We use AI coding tools for development and review, and we treat configuring them for a codebase as real engineering work. HOW WE HIRE Intro call. Your background, your motivation and the role. Technical interview. Built on the Technical depth part of the Required section and the technical items in What you will own . People and delivery. Built on the Management experience , People leadership and Delivery and stakeholders parts of Required : real situations from your experience with hiring, performance, delivery and incidents. Bar raiser. An engineer from outside the hiring team checks that you raise the overall engineering bar. BENEFITS We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes medical, dental, and vision, PTO, and a personalized career roadmap for each employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we're not just building a business; we're cultivating a community that values creativity, collaboration, and the transformative power of play. EMPLOYMENT DETAILS Location: Remote  Employment Type: Full-time Schedule: 10:00–19:00 (flexible) Contract: Employment agreement (entity or EOR arranged per candidate location) EQUAL EMPLOYMENT STATEMENT Xsolla is dedicated to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, language, sexual orientation, national origin, genetics, political beliefs, disability, age, marital status, social background or other characteristics. We are committed to compliance with all fair employment practices regarding citizenship and immigration status. CONTACT If you have questions regarding our hiring practices, please contact careers@xsolla.com. For more vacancies, visit xsolla.com/careers.

Location: Russia; Moscow; Perm Workplace: remote About Xsolla Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. About the Role We are hiring a Staff Software Engineer to own the smart wallet and account abstraction layer of XsollaZK, Xsolla's Web3 platform on a zkSync-based L2. This is an individual contributor role with a large technical scope. You are the technical anchor for how millions of players get an on-chain account they never have to think about. The product. Every player who signs in with Xsolla ID receives a deterministic, non-custodial smart account. The account is derived from their identity claim, is identical on every device, and its private keys never leave the client. It is not a separate wallet screen: it is the player's inventory. It backs: minting and gifting; trading on our Bazaar marketplace; Merkle-based reward airdrops; gas sponsorship; an in-app dApp browser that exposes the same signer to third-party applications through an EIP-1193 provider. Responsibilities The technical strategy and architecture of the smart wallet platform: account model, signing, session management and gas abstraction at scale The smart account and permissioning model, evolved to meet both product and compliance needs The security model where our signer meets third-party applications: what a player is actually approving, and proof that the UI shows it The custody and recovery roadmap, from today's interim mechanisms (including raw key export) to MPC -based custody Cross-team technical decisions on account architecture, SDK contracts, data modeling and platform reliability, with buy-in from the wallet, marketplace, SDK, mobile and security teams Engineering standards in the Web3 domain Production escalations in the wallet and account abstraction domain WHAT YOU WILL DO Design and evolve the ERC -4337 user operation lifecycle (prepare → sign → submit → confirm), EIP -7702 delegation, deterministic address derivation from identity, and the session-key and permission primitives (including origin-bound Smart Sessions) that let players act without a prompt on every transaction Set the paymaster and gas-sponsorship strategy: what we sponsor, how we meter and cap it, how we defend it from abuse, and how it degrades when sponsorship is unavailable Define EIP -1193 / EIP -6963 provider behavior, origin-bound and policy-versioned sessions, trust tiers, and the transaction and signature confirmation flow Design recovery paths (passkeys, guardians, social recovery) that a non-crypto-native player can complete without losing assets Identify systemic risks (key management, signature phishing, reorgs and indexer staleness, bundler and RPC dependencies, contract upgrade paths) and lead initiatives that resolve them before they become incidents Write RFCs and design docs; review critical code, contracts and designs Work with product, security, legal and infra teams to align on the roadmap and turn business and regulatory needs into well-scoped technical plans Create leverage for the engineering org through documentation, tooling and mentorship Serve as the escalation point for complex production issues: a stuck user operation, a drained paymaster, a signing prompt a player didn't understand Set up and maintain AI tooling in the repositories (Claude Code, Codex, Cursor: rules, context, harness) so the team uses it effectively REQUIRED Experience 8+ years of engineering in the Ethereum ecosystem working with crypto wallets and smart wallets Designing or operating a production smart wallet or account abstraction system serving real users and real value Shipping and operating systems where a bug meant lost funds. You can talk concretely about the guardrails, reviews and rollback paths you put in place Account abstraction and smart wallets ERC -4337 in depth: EntryPoint (v0.6 through v0.8), the UserOperation lifecycle, bundler and paymaster roles, validation rules ( ERC -7562), gas estimation and its failure modes, counterfactual deployment, signature validation for undeployed accounts ( ERC -6492) EIP -7702 and the post-Pectra landscape: EOA delegation, its interaction with EntryPoint v0.8, and the risks it introduces (residual key authority, delegation phishing, storage collisions) Building on or extending production smart account implementations; modular account standards ( ERC -7579 / ERC -6900) Session keys and delegated permissions: scoped, time-limited, policy-bound authority; TTLs and contract allowlists, origin binding, permission revocation; ERC -7710 / ERC -7715 Signature schemes and wallet interfaces: EIP -712 typed data, ERC -1271 contract signatures, WebAuthn / passkey (P-256) signers; EIP -1193, EIP -6963, EIP -5792 batched calls, ERC -7677 paymaster interfaces Smart contracts and EVM Solidity and the EVM : reading, reviewing and writing account, paymaster and module contracts; gas-aware design; upgradeability and migration patterns; working effectively with external auditors L2 specifics and their effect on wallet design: native account abstraction on zkSync-family chains, transaction and fee models, finality and reorg behavior, RPC and indexer semantics Backend Go: idiomatic code, concurrency patterns, performance profiling TypeScript and SDK design: client-side SDK surfaces that internal and external teams build on Distributed systems and their trade-offs (consistency, availability, failure modes); reconciling off-chain state against an authoritative on-chain result Data and infrastructure PostgreSQL: schema design, query optimization, migrations at scale Kubernetes: deploying, operating and debugging services Asynchronous messaging and event-driven design (Kafka or NATS ): consumer groups, at-least-once delivery Git, CI/CD Security Key management in production: platform secure storage (Keychain / Keystore), HSMs, MPC or threshold signing, and the operational discipline around each Wallet threat modeling: signature and delegation phishing, malicious dApp origins, replay and cross-chain replay, token approval abuse, paymaster griefing and DoS Leadership Leading multi-quarter technical initiatives across teams Influencing architecture and standards beyond your immediate team RFCs and design docs that stand on their own and are actionable without you in the room AI-assisted engineering Daily hands-on use of AI coding tools (Claude Code, Codex, Cursor) for development and review, including configuring them for a repository English English at C1 level Nice to Have Hands-on experience with ZK Stack and zk -rollups Hands-on experience with embedded and ecosystem wallet and AA infrastructure technologies (thirdweb, Privy, Dynamic, Turnkey, Pimlico, Alchemy, Biconomy, ZeroDev, Safe{Core}), with clear views on build versus buy MPC / threshold custody, social recovery or guardian-based recovery in a consumer product NFT and token infrastructure at scale: ERC -721 / ERC -1155 / ERC -20 mechanics, marketplace contracts, Merkle-based airdrop and claim distribution, chain indexing services Contributions to open-source wallet, security or identity projects, including authoring or reviewing ERC / EIP standards Compliance requirements for crypto consumer products: KYC / AML , geofencing, MiCA , SOC 2, ISO 27001, GDPR data minimization, audit logging Identity and auth integration for wallet derivation: OAuth 2.0 / OIDC , JWT verification, token scoping Platform or infrastructure engineering: building systems other engineers build on Owning a mobile wallet surface: React Native / Expo, WebView provider injection, native signing UI Gaming industry experience: platforms for game developers, publishers or players How We Work Decisions in writing. Significant technical decisions are written up as an RFC or ADR that lays out the options and trade-offs. We work across time zones, so a design has to stand on its own. AI is part of everyday engineering. We use AI coding tools for development and review, and we treat configuring them for a codebase as real engineering work. HOW WE HIRE Intro call. Your background, your motivation and the role. Technical interview. Built directly on the What you will own and Required sections above. We won't ask about anything this posting doesn't promise. System design. You design part of a wallet or account abstraction platform end to end and walk us through the trade-offs. Bar raiser. An engineer from outside the hiring team checks that you raise the overall engineering bar. Benefits We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes medical, dental, and vision, PTO, and a personalized career roadmap for each employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we're not just building a business; we're cultivating a community that values creativity, collaboration, and the transformative power of play. EMPLOYMENT DETAILS Location: Remote  Employment Type: Full-time Schedule: 10:00–19:00 (flexible)

О компании OperAI — интерактивная система на базе ИИ для управления операционной деятельностью компании. Цель — создать «второй мозг для CEO», сделать процессы прозрачными и управляемыми, помочь руководителям видеть риски, договорённости и реальное состояние компании. Сейчас запускают пилоты с первыми командами и проверяют продуктовые гипотезы. Требования: — Опыт в продуктовой или data-аналитике от 3 лет — Опыт проектирования архитектуры данных и построения аналитической инфраструктуры с нуля — Умение создавать модели, схемы и пайплайны для сбора, обработки и хранения структурированных и неструктурированных данных, в том числе с AI — Практический опыт работы с неструктурированными данными: переписками, расшифровками встреч, документами и корпоративными коммуникациями — Опыт построения процессов разметки данных: разработка схем разметки, критериев качества и инструкций для разметчиков — Уверенное владение SQL и Python для анализа и обработки данных — Понимание ETL/ELT-процессов, требований к качеству данных и принципов Data Governance — Глубокое понимание принципов работы LLM, embeddings, RAG и AI-агентов — Умение проектировать метрики и системы оценки AI-функций: точность, полнота, галлюцинации и реальная польза для пользователя — Готовность брать на себя ответственность и строить аналитическую функцию продукта с нуля Задачи: — Проектировать систему внутренней аналитики — Проектировать структуру хранения данных: события, встречи, задачи, решения, коммуникации и связи между ними — Помогать формировать единую модель данных для AI-функций продукта — Оценивать качество работы LLM: точность извлечения фактов, полноту, галлюцинации и полезность результатов — Разрабатывать подходы к разметке данных и созданию датасетов для тестирования AI-системы — Участвовать в проектировании AI-пайплайнов, RAG, embeddings и систем поиска по корпоративным данным — Находить закономерности в операционных процессах клиентов и превращать их в продуктовые решения — Определять ключевые продуктовые метрики и строить дашборды для команды и фаундеров — Анализировать поведение пользователей, пилотные запуски и продуктовые гипотезы — Вместе с разработчиками улучшать качество, наблюдаемость и надёжность продукта Будет большим плюсом: — Опыт проектирования архитектуры AI- или ML-систем — Работа с LLM API, векторными базами, LangChain/LangGraph или аналогичными инструментами — Опыт построения систем мониторинга и оценки качества LLM — Понимание операционной деятельности компаний: задач, встреч, решений, ответственности, сроков и отчётности — Опыт работы с BI-инструментами и продуктовыми системами аналитики — Опыт в B2B SaaS, стартапах или продуктах для руководителей и команд — Опыт взаимодействия с data engineering, ML и backend-командами Условия: — Equity-модель компенсации на начальном этапе — опцион — Возможность с нуля построить аналитику AI-продукта — Высокое влияние на архитектуру, продуктовые решения и развитие компании — Прямая работа с фаундерами — Пилотные клиенты и реальные данные для проверки гипотез — Гибкий формат работы и быстрый цикл принятия решений — Возможность вырасти в Head of Data / AI Analytics по мере развития проекта

Задачи: — Устранение деградации производительности при росте объема данных — Работа с legacy-системой ETL-обработки и загрузки в Db2 Требования: — Значительный опыт работы с C — Опыт с реляционными базами данных, в идеале с Db2, Embedded SQL — Intel OpenMP / multithreading — Опыт performance optimization — Опыт работы с legacy-системами — Опыт AI девелопмента Условия: — Удалённая работа с графиком 12:00–21:00 или 13:00–22:00 по Армении (GMT+4) — Проект на зарубежную организацию с legacy-системой обработки данных — Старт ASAP — Гражданство РФ, локация не РФ и не РБ

Location: Istanbul, Türkiye Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. About the Opportunity We are looking for an experienced risk leader to lead the risk management function at OKX Türkiye. You will own the local enterprise risk management framework and provide independent oversight and challenge to ensure risks are identified, assessed, managed and reported within Board-approved risk appetite. Working with local management and OKX Group functions, you will oversee risks arising from local operations, intra-group services and external providers. You will advise senior management, the Risk Committee and Board on the firm’s risk profile, emerging risks and remediation priorities. The role requires sound judgement, the confidence to challenge decisions, and a strong understanding of Turkish capital markets, operational resilience and financial services controls. Experience with crypto asset platforms and custody is highly desirable. What You’ll Be Doing Lead and develop the local risk function, embedding clear accountability, independent second-line oversight and a strong risk culture across OKX Türkiye. Own and maintain the enterprise risk management framework, policies and procedures, aligning Group standards with local business needs and applicable Turkish requirements. Lead enterprise risk assessments and Risk and Control Self-Assessments with business and Group teams; maintain the risk register and challenge control effectiveness and residual risk. Develop and recommend risk appetite and limits for Board approval; maintain key risk indicators, monitor exposures and promptly escalate breaches and material concerns. Present clear, decision-focused risk reports to senior management, the Risk Committee and Board, highlighting exposures, trends, incidents and overdue remediation. Provide independent oversight of technology, cybersecurity, data and crypto asset custody risks, challenging control design and effectiveness with specialist Group and local teams. Partner with Compliance and Legal on regulatory developments, financial crime and customer protection risks, maintaining clear responsibilities and independent risk challenge. Oversee intra-group outsourcing and third-party risk, including due diligence, service performance, concentration risk, contractual controls and exit arrangements. Work with Finance and Group specialists to develop and oversee capital, liquidity, market, credit and counterparty risk controls proportionate to the local business, including stress testing. Oversee operational resilience, business continuity and disaster recovery readiness; challenge critical service dependencies, recovery plans, testing and incident lessons learned. Assess risks from new products, business changes and emerging crypto market developments; oversee incident and issue escalation and validate effective remediation. Act as the senior risk contact for local management and relevant regulatory engagement, working with Compliance and Legal; coordinate audit and regulatory findings and ensure timely risk remediation. What We Look For In You Must hold a valid Capital Market Activities Level 3 Licence (Sermaye Piyasası Faaliyetleri Düzey 3 Lisansı) as a minimum and maintain it in good standing throughout employment. Approximately ten years’ relevant risk management experience in financial services or capital markets, including senior leadership or ownership of a risk function; brokerage or crypto asset experience is particularly relevant. Strong knowledge of the Turkish capital markets regulatory framework and the Capital Markets Board (SPK), with the ability to translate evolving crypto asset requirements into practical risk controls. Proven expertise in enterprise and operational risk, outsourcing governance, operational resilience and financial risk, with the ability to challenge technology, security and custody specialists. Strong analytical judgement and communication skills, with experience presenting to Boards and senior committees, engaging regulators and influencing stakeholders across an international group. A bachelor’s degree in Finance, Economics, Risk Management or a related discipline. Additional certifications such as FRM, CFA or CAMS are desirable. Fluent Turkish and English, with the ability to work effectively with local stakeholders and global teams. Fluent in the use AI tools to improve risk analysis, reporting and decision-making. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependents More that we love to tell you along the process! Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Location: British Columbia Workplace: Hybrid Safe Software is looking for an experienced developer to join our Cloud Applications team and take a leading technical role in our FME AI Service, AI Assist Plugin, and future AI product offerings. This is deliberately a hybrid role. We need the software engineering depth to build and operate production services, alongside the experimental instinct to determine empirically what makes an LLM-based agentic system work. Many of the problems don't have standard solutions yet, so you'll be the person who designs the experiment, measures the result, and turns the finding into a shipped improvement. We have an immediate opening and are excited to find the right candidate to join our team. ABOUT THE TEAM As a member of our Cloud Applications development team, you will take technical leadership of designing and developing new features, as well as maintaining existing functionalities in our products. You will primarily work in Python (FastAPI and LangChain), with a willingness to learn new languages and technologies as needed. You will also contribute to support our live applications. The team practices agile development and utilizes Kanban boards to manage their backlog, epics and stories. As a Staff Software Developer - AI Products, you will regularly participate in team meetings (stand-ups, planning), lead technical decision-making processes, and work closely with our Product Managers, UX Designers, and QA, as well as Customer Experience team members who review live traces from AI applications. WHAT YOU’LL BE DOING As a Staff AI Developer, you will: - Design and build server-side services and APIs (FastAPI). - Design and build modular agentic workflows to augment the FME platform capabilities. - Stay up to speed with developments in AI and LLM tooling, and incorporate relevant ideas into our products. - Participate in every aspect of the development cycle, including shaping specifications with the product owner, developing clean and maintainable code, writing unit and integration tests, doing code reviews, and maintaining and improving existing code. - Collaborate, support, and share expertise with other team members. - Work closely with the product owner and design team to deliver a high quality and consistent user experience. - Maintain and continuously improve the building and delivery of our AI service to reduce query latency and improve assistant response quality. - Investigate, evaluate the impact, and resolve anomalies and bugs in live applications. - Participate in the team’s light on-call rotation to support live applications. QUALIFICATIONS, SKILLS, AND COMPETENCIES Safers come from a variety of backgrounds with a diversity in skills and knowledge. That said, we find that those who are most successful in this role have experience with the following areas. Background - Bachelor's degree in Computer Science or a related field, or equivalent experience. - Typically, at least 7 years of experience working on production backend systems. - A track record of leading technically ambiguous projects and raising the capability of the engineers around you. Technical skills - Project or work experience with agentic LLM-based applications, including techniques such as RAG, prompt and context engineering, and tool calling. - Demonstrated experience evaluating LLM systems: building eval sets, defining quality metrics, and tracing and debugging non-deterministic behaviour. - Depth in at least one modern backend language, and the ability to pick up others as the work demands. - Experience with a modern backend web framework such as FastAPI. How you Work - An empirical mindset — comfort designing and running experiments to reach a decision, rather than reasoning about which approach should be best. - Strong interest in the craft, a curious mind, and eagerness to learn new tools and technologies. - Able to explain technical tradeoffs clearly to teammates and stakeholders. - Excellent organizational skills, with the judgment to prioritize your own work efficiently. - Resourcefulness and the ability to work well in a team-based environment. DESIRED SKILLS / BONUS POINTS We don't expect any single candidate to have all of these. Evaluation and experimentation: - Familiarity with LLM evaluation libraries such as Ragas, DeepEval, openevals, promptfoo, Inspect, or TruLens. - Experience with LLM observability and tracing tooling such as LangSmith, Langfuse, Braintrust, or Arize Phoenix. - Statistical literacy: A/B testing, significance, and sample sizing for model and prompt comparisons. - A background in machine learning or data science, or experience collaborating closely with those who do. - Experience with the problem domain: FME, other ETL tools, spatial data processing, or data integration. Retrieval and infrastructure: - Working knowledge of embeddings, vector databases (e.g. pgvector), and chunking and retrieval strategy. - Experience with Docker and deploying applications on Kubernetes. - Experience working with services from a cloud provider, such as AWS, GCP, or Azure. - Understanding of common client and server side vulnerabilities (e.g. OWASP Top 10) as well as security vulnerabilities specific to LLM–based applications. WHAT YOU CAN EXPECT When you choose a career with Safe Software, you’re also choosing… - Meaningful Work: Make a profound impact across our business, workplace and data integration product. - A Supportive Environment: Feel empowered to share your ideas and implement them with high autonomy and team support. - Social Responsibility: Become part of a team that finds meaningful ways to give back https://www.safe.com/about/community-initiatives/. Take paid time off to volunteer for one of our Safe-organized opportunities that align with our core community initiatives. - Learning & Career Development: We believe in your continuous learning and growth. Take advantage of an annual learning budget and training programs paid for by Safe. - Flexible Working Hours: Flexible and remote-friendly work arrangements to fit your lifestyle. Work when you want, and how you want to. - Work-Life Balance: There's no place for burnout at Safe. Maintain a healthy balance of your personal and work life without splitting yourself in two. Enjoy 3 weeks of vacation to recharge, plus an additional paid 6 seasonal days off per year. - Health & Wellness: Extended health, dental, health or lifestyle spending, and counseling benefits from day 1. That’s right, no waiting period. - Family is important: We are dedicated to supporting our employees through parenthood and offer a Parental Leave Top-Up Program for new parents through childbirth or adoption. - Shared Success: Share in Safe’s success with our bi-annual profit sharing and RRSP/TFSA matching program. - An Accessible Commute: Regardless of your commute method, we’re located close to public transit, and provide complimentary parking and bike storage for our team! ABOUT SAFE SOFTWARE Safe Software transforms organizations with FME, the only All-Data Any-AI Enterprise Integration Platform connecting all your data, anywhere, at any velocity. With over 30 years of expertise and 25,000+ enterprise customers across 125+ countries, we simplify your data journey, wherever it leads. Founded in 1993, Safe is headquartered in Surrey, BC with over 300 team members and counting. We’re always looking for talented individuals with diverse backgrounds who are determined to learn and grow. Are you ready to join the team? OUR COMMITMENT TO DIVERSITY AND INCLUSION Safe Software is an equal opportunity employer and we truly believe that innovation and strength begin with diversity and inclusion. We welcome all candidates regardless of race, gender identity or expression, sexual orientation, age, ability, disability, national or ethnic origin, political belief, religion, or family status. Should you require accommodations during the recruitment process, please contact hr_dept@safe.com. NOTICE REGARDING USE OF AUTOMATED TOOLS IN HIRING At Safe Software, we use automated tools to help us process applications and identify qualified candidates. These tools may assist in screening resumes and summarizing interview content based on the information you provide. This helps our team review applications efficiently while ensuring every candidate is considered based on the requirements of the role. Please be assured that the final hiring decision is always made by a member of our team. For more information on how we protect your privacy, please see our policy https://www.safe.com/legal/#applicant-privacy-policy. If you have questions about this process, please contact us at hr_dept@safe.com.

Location: New York, NY (HQ); San Francisco, CA; Remote (US) Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT PRODUCTION ENGINEERING Production Engineering is Ramp's infrastructure ownership layer. We exist to make Ramp faster, more reliable, and more scalable — and we do that by being embedded in the problems, not adjacent to them. A few things that define how we operate: - One team, one company, one objective. There is no "infra team" and "product team" — there is Ramp. We share the company's goals as our own. When a product team struggles with reliability or scalability, that is our struggle. - If reliability or scalability is at risk, we own it. We don't wait to be invited, and we don't ask whose code it is. If a system is slow, if it breaks, if it won't scale — that's ours to lead, regardless of where it lives in the stack. - We go first, and we go fast. When the path isn't obvious, we don't wait for someone else to find it. We move with urgency, propose the solution, align the stakeholders, and stay in until it's done — not until our ticket is closed. - We lead the way. We find the next problem before it finds us. And when we solve it, we don't just fix it for ourselves — the patterns and standards we establish become the foundation the rest of Ramp builds on. That's not a side effect of the job; it's the job. - We stay calibrated. Speed means nothing if we're moving in the wrong direction. We regularly stop and ask honestly whether what we're working on is still the highest-leverage thing we could be doing — the discipline that makes sure our effort compounds toward what actually matters. You cannot build the future of finance on shaky infrastructure. OUR TEAMS Production Engineering is organized into teams — but we think about teams differently. Teams are mutable. We build them around what needs to be done, not around what we need to do given the teams we already have. The structure below reflects our current priorities; it will evolve as Ramp does. Right now, we operate across four areas: - Compute — the foundation everything runs on: container orchestration, networking, load balancing, edge infrastructure, and the deployment systems that get code from engineers' laptops to production reliably and at scale - Storage — databases, caching, object storage, and the data infrastructure that underpins everything - Workflows & Messaging — the systems that power Ramp's financial workflows and event-driven architecture - Developer Infrastructure — the platform that makes every builder at Ramp faster and more autonomous: observability, cost attribution, and CI/CD systems that give teams visibility into what they build and what it costs WHAT YOU'LL DO Production Engineers at Ramp are full software engineers who happen to specialize in infrastructure. You write production code, you own systems end-to-end, and you drive technical outcomes across the organization — not just within your team. As a Tech Lead & Manager, you are a hands-on player-coach: you will lead and grow a team while remaining directly engaged in the technical work. Management at Ramp does not mean stepping away from the technology or the code. You will help set direction, develop engineers, and work alongside the team to deliver consequential infrastructure outcomes. Day to day, you will: - Build, coach, and grow a high-performing team. Create clarity, give direct feedback, mentor engineers, and help each person expand their technical judgment, ownership, and leadership. - Set technical direction for your team’s domain. Identify the highest-leverage problems, define a path forward, bring the right people along, and stay engaged with the team until the work is delivered. - Build and operate critical infrastructure across Ramp's compute, storage, messaging, and observability stack — owning the systems that handle real financial transactions at scale. - Drive architectural change — not just flag problems. When you surface a reliability or scalability issue, you own the path forward: you propose the solution, find the owners across engineering, and stay in until it's resolved. - Partner with product teams at the design phase — reviewing architectures, embedding golden paths, and making it easy to build correctly the first time. - Build Ramp's next level of scale — you'll be a hands-on contributor to the most consequential infrastructure shift happening right now: our move to a cellular architecture, enabling Ramp to scale, reach international markets, operate in highly regulated and constrained environments (e.g. FedRAMP), and deliver on enterprise-grade SLAs. - Enable AI-native engineering — as Ramp builds increasingly AI-powered products, PE is the team that makes sure the platform can support them. You'll proactively partner with product teams on AI infrastructure patterns, define the golden paths that turn one-off solutions into reusable foundations, and stay ahead of emerging challenges before they become blockers. - Build developer tooling and self-service infrastructure — so that other teams can answer their own questions (cost, performance, reliability) without involving PE. - Participate in on-call rotation — and more importantly, use every incident as a signal to eliminate the root cause, not just resolve the symptom. - Lead across the company — PE doesn't just review designs or show up when called. We proactively initiate cross-team architectural reviews, and we take full ownership of company-wide reliability and scalability initiatives: identifying the problem, proposing the solution, aligning the stakeholders, and staying in until it's done. WHAT WE LOOK FOR We don't hire for a checklist. We hire for the instincts that make someone exceptional on this team. The mindset we're looking for: - You love building teams, mentoring engineers, and helping people grow—and you are equally energized by writing code, reviewing designs, and digging into complex distributed systems. - You are a true player-coach. You know when to coach, delegate, and create space for others to lead; you also know when your direct technical involvement will create the most leverage. - You can set a technical direction, bring people along, and then lock in with the team to make it happen. - You do not believe managing engineers means stepping away from the technology or the code. - You can't walk past something broken without wanting to fix it — and you don't stop at the workaround. You find the root cause and eliminate the class of problem. - You think in systems, not tasks. You understand that "fixing the bug" is step one; making it so the bug can't happen again is the job. - You move with urgency but without chaos. You know when to go fast and when to slow down and do it right. - You treat product teams as your users. You build for them, communicate with them proactively, and measure success by how much faster they can ship. But you're also an owner — when something is broken or blocking, you don't wait to be asked. We win when our customers win. - You leave things better than you found them. Every system you touch, every process you encounter — you move it forward. Not always dramatically, but always directionally. You picked it up in a certain state; you leave it in a better one. - You are equally comfortable reading a database query plan, reviewing a system design doc, writing a Terraform module, and jumping into an incident at 2am. Experience profile: - 8+ years of software engineering experience, including a track record of shipping high-quality architectures for critical systems. - Staff+-level technical leadership: experience setting technical direction and leading complex, multi-quarter technical initiatives from ambiguous problems through durable production outcomes. - Experience managing, mentoring, and developing engineers. You create clarity, give direct feedback, and help people grow in technical judgment, scope, and leadership. - A sustained hands-on engineering track record. You are comfortable writing and reviewing production code, evaluating system designs, debugging incidents, and making difficult technical tradeoffs. - Hands-on experience building and operating distributed systems at production scale. - Strong software engineering fundamentals — you write clean, well-tested, production-ready code and set that standard for the team. - Experience with at least one major cloud provider (AWS preferred). - Familiarity with observability and reliability practices, including SLOs, error budgets, alerting, dashboards, and incident response. - Track record of delivering through cross-functional coordination: aligning stakeholders, unblocking teams, and staying accountable for the outcome. - Comfortable using AI tooling and coding agents as part of your everyday engineering workflow — and helping a team use them to move faster and think bigger. Bonus (not required): - Experience with cellular or multi-tenant architecture patterns - Prior work on workflow orchestration systems (Temporal) - Contributions to developer experience or internal platform tooling - Experience in fintech, payments, or regulated industries (FedRAMP, SOC 2) BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Location: London, UK Workplace: Hybrid ABOUT BEAMERY With the rapid expansion of AI and automation, the future of work has never been more challenging for organizations. Beamery’s unique jobs, skills and tasks data platform helps organizations navigate these challenges and make more informed decisions across Talent Lifecycle Management. Our solutions power recruitment, mobility, upskilling, diversity, work architecture and workforce planning for some of the world’s most forward-thinking companies. We believe that where you work is much more than just a job. Millions of people are being left behind every day in their careers, and we’re on a mission to fix this by creating equal access to meaningful work, skills and careers for all. We are an equal opportunity employer committed to building a representative Beamery, creating an equitable, inclusive and engaging environment for our people. So, what are you waiting for? Join us and help us transform the future of work once and for all. What’s ahead — and why it’s an exciting time to join the team:  Building agentic AI with advanced orchestration (multi-agent workflows, tool use, evaluation loops) that turns complex talent data into decisions. - Training and fine-tuning our own models on unique data about jobs, skills and tasks, and benchmarking them against the best in our market. - Joining an award-winning AI team (ACM RecSys Challenge 2026) that turns new ideas into innovative solutions with scientific rigour. - Sharing our work and learning from industry peers through challenges, conferences and publications. But it’s not all about creating high-quality products, we also very much value the company culture we have worked hard to create; built on trust, empathy & honesty ensuring our workforce is able to bring their full selves to work. ABOUT THE OPPORTUNITY AT BEAMERY We are looking for an experienced Senior Product Manager to own Beamery's Data Foundry end to end. The Data Foundry is our proprietary data platform, powering existing and emerging product lines including our Talent CRM and Workforce Suite. Through this, we provide data-as-a-service to customers through channels such as MCP/API. This is an emerging commercial channel. Its core capabilities span the data models, taxonomies and integrations that underpin talent and workforce decisions across our platform. The Senior Product Manager, Data Foundry will treat data both as infrastructure embedded within our products and as a product that customers buy in its own right. Beyond internal embedding and direct commercial sales, the platform is open — customers and partners can build on top of it through MCP and API access. You will work with product leadership to set the strategy, agree what the platform provides internally, decide what becomes customer-facing, and define how the platform opens up for customer and partner innovation. You will work at the centre of the platform, creating a balance between two essential initiatives. On one side, you serve the Talent CRM and Workforce Suite product managers as your customers, agreeing the data contracts they build on. On the other, you partner with the data science, data engineering and integrations engineering leads to turn those needs into delivery. WHAT YOU WILL BE DOING Ownership and strategy - Own the Data Foundry vision, strategy and roadmap, framing each item as a bet with a clear driver, aligned to overall strategic objectives - Ensure alignment of Data Foundry strategy to Beamery's AI strategy, reflecting the shift from providing AI assistants to providing AI access through MCP and API channels. - Decide what Data Foundry provides as internal infrastructure and what is packaged and sold as a customer-facing product. - Make and defend build versus buy decisions across integrations, analytics, AI and taxonomy capabilities. Discovery and commercial impact - Discover and validate what external customers want from data-as-a-service, and partner with Sales and Customer Success to drive adoption with clear GTM and enablement plans - Build the case for Data Foundry as a new commercial channel, defining and proving the standalone commercial model alongside its role as platform foundation, quantify the value it unlocks for specific customers and deals, and measure success against value and usage metrics. Partnership and delivery - Lead your team to deliver solutions our customers can’t imagine working without and that deliver measurable customer and business outcomes - Agree and maintain the data contracts with the CRM and Workforce Suite product managers, treating them as internal customers with real commitments. - Prioritise across data science, data engineering and integrations engineering, translating tribe needs into a clear and deliverable plan. Communication & alignment - Communicate transparently and create alignment between peers and leadership on priorities, trade offs and progress - Manage customer and sales expectations through well managed structured cadences Data governance and trust - Own how workforce data is governed, secured and access-controlled as it is exposed through APIs and MCP, meeting or exceeding enterprise privacy and compliance expectations. - Ensure the outputs of Data Foundry's models are measurable and explainable, so customers and internal teams can trust and act on the results. WHAT WE ARE LOOKING FOR - A product manager with a minimum 6 years experience in complex technical, data or AI products, a significant portion of which should be in enterprise B2B SaaS. - Strong grasp of data platforms, APIs and integrations, comfortable making architecture-adjacent decisions while collaborating with and holding priorities with engineering and data science leads. - Experience handling sensitive or regulated data, with a clear view of governance, security and privacy. - An analytical thinker who uses data to find opportunities, confidently prioritise and measure success, balancing between competing tribe roadmaps and internal customers. - Commercially fluent, experience with 0-1 product stages, shaping early product with new go to market motions, able to connect product decisions to revenue, cost and customer ROI. - A clear communicator who builds trust across product, data science and engineering, and who is comfortable taking a position and explaining the reasoning. - Experienced owning and delivering AI capabilities, with a keen interest in how AI is reshaping the talent and workforce space, and in giving customers trusted, explainable access to their data. Beamery is for Everybody. Diversity and open expression are fundamental to us. We acknowledge the challenges in our industry and strive to develop an inclusive culture where everybody can contribute. We are dedicated to creating an inclusive environment for everyone, regardless of ethnicity, religion, color, sexual orientation, gender identity, race, national origin, age, disability status, or caregiver status. If, for whatever reason, you need us to make reasonable adjustments and adaptations to our recruitment process, please email accommodations@beamery.com Visit our Diversity, Equality and Inclusion page https://careers.beamery.com/life-at-beamery/diversity-equality-inclusion/to learn more about progress and commitments.

Location: United States Workplace: Remote About Cantina: Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future! About the Role: Cantina is a place where people create AI characters and the videos those characters star in, and the catalog grows far faster than anyone can browse. Search and recommendations are what turn that catalog into a product: they decide which characters a new user meets in their first minute, which creators get discovered, and what appears in every feed, leaderboard, and discovery surface we ship. We are hiring a Senior Staff Software Engineer to own that area end to end — retrieval, ranking, relevance quality, and the roadmap for all three. This is the first dedicated hire for search and recommendations, so you will inherit a real production system serving live traffic along with a large amount of unclaimed ground, and you will set the technical direction rather than execute someone else’s. What you'll do: - Own the architecture and roadmap for search and recommendations end to end — query understanding, retrieval, ranking, and the surfaces they feed: search, discovery, the home feed, and our trending and creator leaderboards. - Make relevance a measured discipline rather than an opinion: define the quality metrics per surface, build the offline evaluation and golden-set tooling, and run the online experiments that decide whether a ranking change ships. - Design and evolve the ranking stack — engagement signal pipelines, decay and freshness models, and the pattern we use to combine pre-computed index-time signals with live re-ranking at query time. - Own OpenSearch in production: index and mapping design, reindex and cutover safety, query performance, cost, and the operational headroom of the clusters behind every discovery surface. - Build candidate generation and personalization for recommendation surfaces, and partner with data and ML on the feature pipelines and models behind them. - Work directly with Product and Trust & Safety on what “good” means for each surface — quality gates, cold-start behavior for brand-new creators and characters, and what must never be recommended. - Set the engineering standard for the area: instrumentation, flag-gated rollouts, and runbooks that let other teams change ranking behavior without breaking it. - Act as the technical point of contact for discovery across backend teams, and mentor the engineers whose work touches it. What you'll bring: - 10+ years building production software, with substantial depth in search, ranking, recommendations, or ML-driven relevance on a consumer-scale product. - Hands-on expertise with a production search engine — OpenSearch, Elasticsearch, Lucene, Vespa, or similar — covering index and analyzer design, relevance tuning, and operating the cluster, not only querying it. - A rigorous approach to relevance measurement: you have owned offline evaluation, golden sets, and A/B or interleaving frameworks, and you can explain what moved a metric and why. - Fluency across the recommender stack — candidate generation, feature pipelines, embedding retrieval, re-ranking — and a working understanding of the serving constraints that make an offline win fail online. - Strong distributed systems and backend fundamentals, with production experience in Go or a comparable systems language, and comfort in the data layer behind it (SQL warehouses, streaming and batch pipelines). - Demonstrated ownership of an ambiguous area: you have picked up something with no dedicated owner, decided what mattered, and can show the result. - Clear written communication and the judgment to work with Product on real tradeoffs between relevance, freshness, safety, and latency. - Preferred: experience with AI-generated or rapidly growing content catalogs, severe cold-start conditions, or ranking under trust-and-safety constraints. Compensation: The anticipated annual base salary range for this role is between $250,000 - $320,000 (USD). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.   Benefits: - Competitive salary and generous company equity - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support - 401(k) retirement savings plan - Lifestyle spending account – $500/month to use however you’d like - Complimentary lunch and snacks for in-office employees - One Medical membership, and more!

Location: Paris; Zurich; Warsaw; London ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. ABOUT THE RESEARCH ENGINEERING TEAM   The team spans Platform (shared infra & clean code) and Embedded (inside research squads). Engineers can move along the research↔production spectrum as needs or interests evolve.   As a Research Engineer – ML track, you’ll build and optimise the large-scale learning systems that power our open-weight models. Working hand-in-hand with Research Scientists, you’ll either join:   - Platform RE Team: Enhance the shared training framework, data pipelines and cluster tooling used by every team; or - Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, Safety …) and turn fresh ideas into repeatable, scalable code.     What You Will Do   • Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools. • Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs. • Design, implement and benchmark ML algorithms; write clear, efficient code in Python. • Deliver prototypes that become production-grade components for Vibe and our enterprise API.   What We're Looking For   • Master’s or PhD in Computer Science (or equivalent proven track record). • 4 + years working on large-scale ML codebases. • Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s). • Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops. • Strong software-design instincts: testing, code review, CI/CD. • Self-starter, low-ego, collaborative.   WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: New York or Houston DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. Senior Quantitative Power Strategist DRW is seeking a Senior Quantitative Strategist to join the US Power and Natural Gas trading team. This individual will focus on the valuation, structuring, bidding, and risk management of energy transactions, in particular full-requirements and load-following opportunities across US electricity markets. The role will work closely with traders, developers, and other quantitative researchers to build pricing and risk frameworks for complex load obligations. The ideal candidate combines deep knowledge of physical power markets with strong quantitative modeling skills and sound commercial judgment. Responsibilities Develop and maintain quantitative models for US power load auctions, full-requirements transactions, and load-following products. Value complex load obligations by modeling demand, weather sensitivity, hourly shape, customer migration, basis, congestion, losses, capacity, ancillary services, renewable obligations, and other embedded risks. Produce bid recommendations and scenario analyses for utility, municipal, cooperative, and commercial and industrial load opportunities. Design hedge strategies across power, natural gas, capacity, transmission, and environmental products. Work alongside traders to translate market views and portfolio constraints into executable bidding and risk-management strategies. Develop tools for portfolio valuation, position reporting, stress testing, and ongoing performance attribution. Research structural changes in organized power markets and evaluate their implications for load pricing and hedging. Improve market data, forecasting, optimization, and analytical infrastructure in partnership with software engineers and data specialists. Communicate model assumptions, limitations, and commercial implications clearly to trading and senior leadership. Qualifications At least five years of relevant experience in US power trading, quantitative research, structuring, or portfolio management. Direct experience pricing or managing load auctions, full-requirements contracts, or load-following portfolios. Strong understanding of US wholesale power markets, including energy, congestion, capacity, ancillary services, transmission, and environmental products. Solid Experience in Python or a similar language Strong foundation in statistics, probability, optimization, time-series analysis, and Monte Carlo simulation. Bachelor’s, master’s, or doctoral degree in a quantitative discipline such as mathematics, statistics, engineering, computer science, physics, economics, or financial engineering. Strong commercial judgment, intellectual curiosity, and the ability to work effectively in a fast-paced trading environment. Clear written and verbal communication skills. The annual base salary range for this position is $150,000 to $250,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . #LI-JW1

Location: Stockholm, Stockholm, Sweden AI Engineer As an AI Engineer, you will help build and scale the AI infrastructure used across EQT Real Estate, enabling investors and operators to create and remix tools ranging from process automation to advanced analytics and interactive applications. You will work directly with users, own projects end-to-end and help turn successful experiments into shared platform capabilities. About the AI team The team builds the technological foundations that allow people across EQT Real Estate to apply AI directly to their work. We partner closely with investors and operators, building across voice, web and chat surfaces and connecting models to the systems, data and tools required to solve real business problems. About the role You will build AI products and platform capabilities from idea through production. Projects are intentionally small enough for one person to own end-to-end, with business users building alongside the team rather than handing over fully formed requirements. The role combines rapid experimentation with the engineering judgment required to make the strongest ideas reliable, reusable and scalable. • Build AI tools alongside users: identify work that should be easier, collaborate on a first useful version and learn from how it performs in real-world use. • Prototype and ship agentic systems that run on EQT Real Estate’s in-house AI harness. • Turn unstructured, business-critical material into agent-ready information that people and downstream systems can use. • Connect internal systems, vendor products, APIs and data sources so AI systems can access the information and tools they need. • Help turn patterns from successful experiments into shared platform capabilities that can scale across teams and geographies. • Own projects independently while working closely with a senior infrastructure engineer on the platform and technical foundations beneath them. About you You are an experienced software engineer who is already absorbed by what modern models can do and eager to discover what they can do inside real work. You are comfortable with ambiguity, value fast feedback and enjoy the point at which an experiment becomes useful enough to deserve robust engineering. Essential • Strong software engineering skills, including practical experience with Python and modern application development. • Hands-on experience building with language or multimodal models, model APIs, retrieval systems, agents or related AI tooling. • Experience working with APIs, databases, backend services, data pipelines or cloud infrastructure. • An experimental mindset paired with sound engineering judgment about what needs to become reliable, observable and maintainable. • Clear written and verbal communication skills, with the ability to build directly with non-technical colleagues and explain technical concepts simply. • Ability to take ownership of loosely defined problems and move quickly from idea to a useful working product. Nice to have • Experience building or operating agentic systems in production. • Experience developing reusable AI platform capabilities or internal developer tooling. • Exposure to investment, real estate or other data-intensive business environments. Real estate experience is not required. What we offer This is a hands-on role in a small team with significant ownership and direct exposure to the people using what you build. You will have a senior engineering colleague to learn from while independently taking projects from experimentation to production. The environment favors fast feedback, simple explanations and the shortest distance between an idea and something people can use. Inclusion at EQT Our vision for EQT employees is to build high performing & engaged teams. Our competitive edge comes from fostering an environment where every individual feels valued, empowered, and motivated to drive business impact. Our commitment to inclusion is not just about fairness; We understand and believe that being a great place to work drives the best performance.At EQT, inclusion is a business imperative and it's embedded into our talent strategy, decision-making, and culture to ensure that every individual and team operates at their full potential. By doing so, we unlock better collaboration, stronger innovation, and superior investment outcomes. About EQT EQT is a purpose-driven global investment organization focused on active ownership strategies. With a Nordic heritage and a global mindset, EQT has a track record of more than three decades of developing companies across multiple geographies, sectors and strategies. EQT has investment strategies covering all phases of a business’ development, from start-up to maturity. EQT has €‌​​354​‌ billion in total assets under management (€195​‌ billion in fee-generating assets under management) as of 30 September 2026, within four business segments – Private Capital, Infrastructure, Real Estate and Secondaries. With its roots in the Wallenberg family’s entrepreneurial mindset and philosophy of long-term ownership, EQT is guided by a set of strong values and a distinct corporate culture. EQT manages and advises funds and vehicles that invest across the world with the mission to future-proof companies, generate attractive returns and make a positive impact with everything EQT does. The EQT AB Group comprises EQT AB (publ) and its direct and indirect subsidiaries, which include general partners and fund managers of EQT funds as well as entities advising EQT funds. EQT has offices in more than 25 countries across Europe, Asia and the Americas and has more than 2,200 employees. More info: www.eqtgroup.com Follow EQT on LinkedIn , X , YouTube and Instagram

Location: Remote - India AI/ML Solution Architect Education & Assessment Platforms About Pratham International Pratham International is a US 501(c)(3) established to support learning innovations for children and youth in contexts across the globe. We enable the adaptation and spread of proven and scalable education solutions pioneered in India by Pratham Education Foundation to contexts across the global south through programming and technical assistance tailored to local realities. Today, Pratham innovations reach millions of children annually through partnerships across ~30 countries. Through the Pratham Shah PraDigi Innovation Centre (PraDigi), Pratham International also seeks to create technology innovations for low-resource communities, and support models aimed at learning for school, work, and life for children and youth. We work on several cutting-edge projects in the domain of Generative AI, Automatic Speech Recognition, and Natural Language Processing. The team’s innovative technology products include the PadhAI: Reading Assessment App, which uses advanced speech recognition to evaluate children’s reading fluency in 8 Indian languages and 6 global languages; Anytime Testing Machine (ATM), an AI-enabled question generation and grading system that allows learners to be assessed on any topic, anytime; and a suite of AI-powered chatbots designed for diverse learning contexts: from early childhood care, to youth employment readiness, and OCR-based analysis tools that digitize and assess student work at scale. At Pratham International, we build for the long term. Whether it’s ensuring products are truly accessible across rural India and the global south through languages, building datasets to ensure context and accuracy for products, or gathering feedback from the field. Backed by partners like Google.org, Anthropic, and the Gates Foundation, we are committed to building digital public goods that ensure every child is in school and learning well. Role Overview About the work We design and deliver AI systems that governments and large education programmes can actually run: curriculum-aligned item generation, paper assembly, handwriting OCR, rubric grading, and teacher-in-the-loop review. The work is already live as practice assessment in India and is being built as a national exam platform in Rwanda, with further country programmes behind it. We are hiring a Solution Architect who sits between discovery and delivery. You will turn messy workshop notes and ministry requirements into architecture a team can build, secure, cost, and operate — RAG and multi-agent systems, LLM-as-judge pipelines, and the cloud/LLMOps layer underneath. You will Own solution design from first workshop to a delivery-ready blueprint (services, data stores, trust boundaries, cost, ops). Architect LLM pipelines: generation, retrieval over curriculum knowledge graphs, standards/duplication/quality judges, constraint-based paper assembly. Design human + AI workflows with audit, segregation of duties, cycle-pinned config, and no student PII in model calls. Choose and govern models, prompts, eval gates, and fallbacks (including OCR: Azure primary, secondary fallback). Shape the Azure/AWS platform: identity, networking, LLMOps/MLOps, observability, and sovereign-hosting constraints. Run client workshops, write POVs and architecture packs, and stay accountable until the system is in production. About the work Pratham International programmes sit at the intersection of pedagogy, public systems, and production AI. The pattern is consistent across countries; the constraints change. Anytime Testing Machine (ATM) — India, practice and feedback End-to-end assessment for real classroom conditions: generate curriculum-aligned items, digitise handwritten answers (often photographed on a phone), grade against rubrics, and return feedback a teacher reviews before it reaches the learner. Built for high pupil–teacher ratios, mixed language (e.g. Hindi + English), and uneven connectivity. Pilots have already reached thousands of learners, including Second Chance (women returning for Grade 10). Model quality is measured against expert golden sets, not vendor slides. National assessment automation A government-grade exam platform. Phase 1 is item authoring and print-ready paper composition before the exam window. Phase 2 is script intake, OCR at national volume, AI-assisted grading with human confirmation on the pilot, results export, and monitoring/evaluation reporting. Subjects span core and elective streams across the curriculum. Design rules that do not exist in a typical SaaS product: cycle-pinned configuration, embargoed papers, hash-chained audit trails, and integration with national student information and assessment management systems. Curriculum knowledge graphs Structured retrieval corpora (concepts + worked examples) that ground generation and judging. This is the standing public-knowledge exception in an otherwise closed exam system, and the seed of a broader education DPI conversation. AI in TaRL and teacher support AI tools that help teachers group and teach by learning level, with an RCT-shaped evidence bar. Same product family: human agency stays in the loop; the model is a capacity multiplier. Country programmes and the three-year direction Adapt the same engine to new ministries, languages, and exam rules (Kenya and other Global South programmes are in scope). The longer bet is a learning and credentialing engine that recognises competence beyond a single syllabus. This is not a chatbot brief. It is constrained generation, retrieval, judging, document AI, workflow, and public-sector operations. Why this role exists Most enterprise AI dies between the workshop and production. We need one person who owns that gap: someone who can sit with NESA, C4IR, Pratham pedagogy, and engineering in the same week, and leave behind a blueprint the delivery team can execute without inventing policy. The target profile is a practising AI/ML Solution Architect — not a pre-sales slide owner, and not an infra-only platform engineer. The person should have done all three of these: Designed GenAI / NLP solutions, PoCs, and client workshops, and can talk to research-grade issues (eval, agent safety, prompt-injection, model-specific behaviour). Owned the AI platform: LLMOps, MLOps, DevSecOps, SRE habits, cloud migration, and FinOps. Taken messy discovery and turned it into Azure-centred RAG and multi-agent designs that a security team will accept. Key Responsibilities Discovery → architecture Lead solution workshops with programme, ministry, and engineering stakeholders. Convert half-formed requirements into service cuts, data stores, sequence diagrams, decision gates, and open-question lists with owners. Write architecture decision records. When the docs disagree (they will), force a decision instead of coding both. AI system design Design LLM jobs as contracts: inputs, JSON schema, temperature, persist prompt hash / model / tokens / latency. Architect generation grounded in structured curriculum blueprints and knowledge-graph retrieval, not "ask the model for a maths paper." Design LLM-as-judge banks (cognitive-level, relevance, difficulty, language, fairness, standards, duplication, blueprint compliance) with thresholds, one-repair rules, and golden-dataset release gates. Design hybrid assembly: constraint solver first, model only on ties and layout exceptions. Design document AI: primary OCR provider, secondary fallback provider, confidence routing, de-identification before any grading call. Specify eval: inter-rater agreement, rubric-band exact match, first-pass item acceptance, cost per paper cycle. Platform, trust, cost Own the reference architecture on the chosen cloud platform(s): identity, RBAC + segregation of duties, private networking, key management, object storage for scans, vector index, data warehouse for monitoring/evaluation reporting. Draw trust boundaries. Student PII never crosses the model gateway. Prompts are curriculum + knowledge-graph + blueprint content only. Put FinOps on the design: token budgets, OCR cost caps, degradation behaviour when a cap is hit, model routing. Specify LLMOps/MLOps: prompt/version registry, eval regression as a release gate, observability, rollback. Design for sovereign hosting and exam-cycle operations (embargo, watermarking, append-only audit, cycle-pinned configuration). Delivery and thought leadership Stay with the build through the first production cycle. Architecture that cannot be implemented is unfinished work. Produce POVs, sequence packs, and workshop artefacts that can be reused in the next country. Mentor tech leads on where the model should stop and the workflow should start. Success in the first two quarters First 30 days — Orient Complete architecture walkthroughs of every live/in-flight system with the current engineering leads; document current state (services, data stores, trust boundaries) as-is, not as-designed Meet every active stakeholder group once: engineering, programme/domain leads, and any external partner or government-side counterpart Inventory all open technical decisions and disagreements in existing specs; log each with current owner (if any) and status Identify the single highest-risk unresolved design question and flag it upward Day 60 — Diagnose Produce a current-state architecture diagram covering every major pipeline: data flow, model/LLM touchpoints, storage, and identity/access boundaries Assign an owner and target resolution date to every open contradiction identified in month 1 Draft a v1 reference architecture for one representative end-to-end workflow: service breakdown, data stores, identity/access model, model gateway placement, and a first-pass cost estimate Day 90 — Commit v1 reference architecture is reviewed and accepted by engineering and relevant stakeholders (not just self-signed) Can walk any core pipeline end-to-end from memory and name every data store, every quality/eval gate, and every trust boundary in it At least one architecture decision record is published for a previously-contested design question, with the decision forced rather than left open A first cost model exists for the reference architecture, with at least one identified lever for reducing spend if usage scales Day 120 — Harden Core pipeline components (generation, retrieval, judging/eval, or equivalent stages for this domain) have documented contracts: defined inputs/outputs, schema, and logged metadata (model, version, cost, latency) An evaluation harness exists for at least one component, with a defined pass/fail threshold A release gate exists that can block a bad prompt, model, or config change from reaching production Day 150 — Extend The next major system phase or capability is specified to the same rigor as the first: trust boundaries, failure modes, fallback behavior, and cost caps documented before build starts FinOps guardrails are in place for at least one production workload: budget ceilings, degradation behavior when a cap is hit, and a routing or fallback strategy Day 180 — Generalize A reusable design pattern or adaptation pack exists, separating what is fixed in the architecture from what varies by deployment context (e.g. language, hosting environment, external-system integration, content/domain rules) At least one engineering or technical lead has been mentored on where model-based logic should stop and deterministic/workflow logic should take over A second production-readiness review (security, cost, or operations) has been passed on a system this person designed or substantially redesigned Qualifications and Experience Must have 8+ years building software or data/AI systems, with recent experience as a Solution Architect, AI/ML Architect, or Principal/Staff Engineer with end-to-end architecture ownership. Shipped at least one production AI/ML system — RAG, multi-agent, LLM-as-judge, or a comparable classical ML/NLP pipeline at scale — that passed a real security, cost, and operations review, not just a pilot or a slide deck. Can produce a delivery-ready architecture blueprint: services, data stores, IAM, failure modes, evaluation strategy, and cost model — detailed enough that engineering isn't guessing at intent. Deep cloud experience on Azure and/or AWS: comfortable defending a landing-zone design, private-endpoint architecture, and a real monthly compute/inference/OCR spend. Fluent in Python, with hands-on experience in both classical ML (model training, evaluation, feature pipelines) and how LLM APIs, embeddings, and vector search actually behave under production load. Comfortable running a room with non-engineers — domain specialists, IT stakeholders, monitoring/evaluation teams — and translating fluently in both directions. Strong written English; this role's output is documentation that functions as a binding technical contract. Strongly preferred Azure AI (AI Foundry / OpenAI / Document Intelligence), plus one of AWS (Bedrock, SageMaker) or GCP (Vertex, BigQuery). LLMOps / MLOps: prompt + model versioning, MLflow or equivalent, eval harnesses, CI that can block a bad prompt. Document AI / OCR at volume; handwriting and low-quality scans, not only clean PDFs. RAG over structured knowledge (graphs, ToS, textbooks) and hybrid search, not only naive chunking. Education, assessment, public sector, or other high-audit domains (health, finance, identity). Certifications: Azure Data Scientist / Azure Solutions Architect; AWS SAA, ML Specialty, or Data Analytics Specialty. Experience with agent security: tool-use limits, DLP on egress, prompt-injection evaluation that is model-specific. FinOps discipline. You have killed a design because it would not survive the token bill. Nice to have Microsoft Fabric, Databricks, Semantic Kernel / Microsoft Agent Framework, MCP (Model Context Protocol) tool integration for agentic workflows. Constraint solvers or exam-blueprint / item-bank systems for automated test assembly. Multilingual generation and feedback, including code-mixed language pairs (e.g. Hindi–English, Spanish–English, or similar). Experience standing up golden/eval datasets with subject-matter experts. Prior work with government data systems, data-sharing agreements, or in-country/sovereign hosting requirements. What this role is not Not a research-only post. Publications help; shipping is the job. Not a pure platform/SRE hire. Infra is part of the design, not the whole design. Not a prompt engineer who hands off “the architecture” to someone else. Not a people manager of a large team on day one — you influence through artefacts and reviews. How we will assess you We value transparency and respect your time. Our hiring process is designed to evaluate both hands-on technical depth and architectural judgment through practical, real-world tasks. Stage 1: Application & Knockout Screening Submit your CV alongside key screening responses Artefact — send two of: architecture note, eval design, runbook, ADR. Slides without decisions do not count. Stage 2: Initial Conversation (30 mins) An introductory screen covering your background, project context and baseline alignment. An opportunity for you to ask questions about our technical roadmap, partner ecosystem, and vision. Stage 3: Practical Take-Home Written Assessment (2–3 hours) Design exercise on a constrained generation + judge + human-review pipeline (we will use a sanitised exam-authoring brief). We want trust boundaries, eval, and cost, not a model-name dump. Stage 4: Deep-Dive Technical & System Architecture Walkthrough Walkthrough of a system you shipped: what failed in review, what you changed, what it costs to run. Stage 5: Final Stakeholder & Vision Alignment Final conversation with key technical leadership and program stakeholders. Employment Details Function: Solutions / Architecture Reports to: Head of Technology / CTO Expected Start Date: Immediate Location: India (remote-first; travel to client workshops as needed) Experience: 8–13 years (strong 6–8 considered if you have shipped production GenAI) Engagement: Full-time Remuneration: Based on candidate’s experience and skillset Flexibility to work across timezones: Our team spans Latin America to Southeast Asia. Early mornings  or late evenings can be a regular part of the rhythm Why join us? Impact at Scale: Your work will directly support the learning journeys of millions of children. With programs active in 31 countries, you will help build Digital Public Goods that bridge the gap for the millions of children across the Global South who still struggle with foundational literacy Innovation at the intersection of Education and Technology: Lead the charge in redefining learning and learning processes through technology. You will contribute to the design and development of cutting-edge learning tools and AI-driven initiatives, ensuring EdTech isn’t just a buzzword, but a scalable solution for global equity. A Culture of Growth and Evidence: At Pratham, we value rigorous testing and continuous learning. You will have the autonomy to experiment with the latest SOTA models, supported by a 5-year strategic roadmap and funding from partners like the Gates Foundation and Anthropic. High-Calibre, Mission-Driven Team: You will work alongside a global team of experts from institutions like MIT, Harvard, and INSEAD. Our alumni have gone on to lead successful startups and international policy shifts, all while remaining dedicated to the goal of “Every Child in School and Learning Well.” Inclusive and Collaborative Environment: Join a diverse team that spans Asia, Africa, and Latin America. We value innovative thinking and local expertise, ensuring that the technology we build is deeply rooted in the realities of the communities we serve. Pratham International is an equal opportunity employer and encourages all qualified people from diverse backgrounds to apply for positions within our organisation, regardless of race, gender, disability or any other status.

Location: London, UK About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic is building the Forward Deployed Engineering team to help the world's most ambitious organizations adopt Claude in ways that are transformative, safe, and lasting. Our Technical Deployment Leads (TDLs) and Forward Deployed Engineers (FDEs) embed with strategic customers to ship production AI applications that solve real business problems. Our delivery model pairs two roles: FDEs own the technical artifact, and TDLs own the engagement end to end, from statement of work (SOW) through production deployment. As a Manager of Technical Deployment in EMEA, you lead a team of TDLs embedded with strategic customers. You own engagement portfolio health across your team, set the bar for delivery quality, and define where we play: which customers and which use cases are ready for production AI today. Your work will directly influence Anthropic's product direction and establish Anthropic as the trusted partner for AI transformation across the region. This role is based in London. This is a player-coach role. You need to be credible in architecture conversations with a customer's engineering leads and equally comfortable in front of executive sponsors. You will also lead the most complex engagements yourself when the stakes call for it. Most of your impact will come through the people you hire and the standard you hold them to. You won't write production code, but you will pressure-test the technical decisions your TDL team makes with FDEs. Responsibilities Hire, develop, and retain a team of TDLs embedded in engagements across EMEA. Set a high bar for delivery judgment, technical fluency, and customer presence, and run the interview loop that holds that bar. Manage each TDL directly: regular 1:1s, clear expectations, direct feedback, and a growth plan. Build a bench that runs engagements well without you in the room. Define the delivery strategy for your portfolio: which kinds of companies we engage, which use cases we prioritize, and how that differs by market and industry. Hold each TDL accountable for a clear account strategy: what we want to achieve with the customer and the exit gates that tell us we got there. Own portfolio health across your team. Track which engagements are on track, at risk, or closing out, and intervene early when delivery drifts. Run engagement quality reviews with your team. Own customer outcomes across the portfolio. Hold the executive relationship with customer sponsors, run steering committees, and represent Anthropic with senior business, risk, legal, and engineering leaders. Make the staffing call for each engagement. Work with FDE managers to match TDLs and engineers to each customer, and adjust as scope changes. Lead the most complex engagements yourself. Take the TDL seat when engagement size, regulatory exposure, or organizational complexity calls for it, from SOW through production deployment. Set the bar for delivery excellence: SOW quality, MVP scoping, and value measurement. Ensure every engagement has a defensible impact story. Build regulatory requirements into how the team scopes and delivers, working with Legal, Security, and customer risk functions, so compliance shapes the plan from the start rather than blocking it late. Codify reusable solution patterns, evaluation frameworks, and technical playbooks across the team. Turn what works in the field into delivery infrastructure that lets the practice scale, and adapt global playbooks to EMEA markets. Partner with Sales to qualify engagements, shape scope, and inform SOWs. Partner with Marketing and Sales on EMEA go-to-market materials, reference cases, and positioning. Run the team's operating rhythm: engagement reviews, knowledge-sharing, and escalation support, and feed the wider post-sales cadence and dashboards. Set team goals tied to customer outcomes. Bring the field back to Anthropic. Surface delivery lessons and emerging use cases from EMEA to Product and Research, and represent the region in leadership discussions. Consistently uphold Anthropic's values as you lead your team. TDLs should be strong representatives of Anthropic's culture as they work within customer environments. Travel to customer sites across EMEA (25–50% expected), with occasional travel to the US. You May Be a Good Fit If You Have 8+ years leading enterprise AI/ML delivery, whether you came up as a founder, engineer, data scientist, or researcher and moved into deployments, with at least 3 years directly managing people who owned delivery outcomes. Have a track record of building and leading technical delivery teams in enterprise environments across more than one EMEA market, ideally including embedded or forward-deployed teams working at client sites. Have delivered AI/ML or LLM-based agentic solutions into production. You understand solution patterns, integration approaches, and what breaks in real environments. Bring depth in at least one industry (e.g., financial services, healthcare and life sciences). You understand how security, compliance, and regulatory review shape what ships, and you can speak credibly with a CIO, a chief risk officer, and a head of engineering. Know the EMEA enterprise landscape: how buying and procurement differ across markets, and how regulatory requirements affect what customers can deploy. Can navigate architecture discussions with engineering stakeholders, evaluate technical trade-offs, and judge whether a proposed agent design is realistic. Have experience with value-based scoping, including TCO/ROI quantification and post-deployment impact measurement. Thrive in ambiguity and bring structure where none exists. You have built a team, practice, or function from an early stage. Have executive presence: polished, credible, and concise with senior leaders in high-stakes settings, and just as effective in a hands-on engagement review with your team. Have strong written and verbal communication skills. You can translate technical complexity into clear narratives for diverse audiences. Working proficiency in a second European language such as German or French is a plus. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £190,000 — £230,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Мы ищем: Талантливого NLP-разработчика на стажировку, которому предстоит работать над интересными высоконагруженными проектами. Требования: — Опыт разработки на Python от 1–2 лет; — Базовое понимание LLM и AI-агентов; — Практический опыт или уверенное понимание RAG-подхода; — Опыт работы с векторными БД или embeddings (FAISS / Qdrant / аналоги — базовый уровень); — Опыт backend-разработки (FastAPI / Flask); — Понимание REST API, JSON, асинхронности; — Готовность развиваться в AI-направлении. Будет плюсом: — Опыт работы с LLM API (OpenAI / open-source модели); — Знакомство с LoRA / PEFT (pet-проекты, эксперименты); — Базовый опыт работы с брокерами сообщений (Kafka, RabbitMQ, NATS); — Docker (базовый уровень); — Pet-проекты с AI-агентами или RAG. Условия: — Работа в команде экспертов; — Широкий технологический стек, сотни проектов; — Гибкий график работы, который позволяет высыпаться и выделять время на хобби; — Возможность разрабатывать IT-решения для разных отраслей, пробовать новые технологии; — Организована удалённая работа, при этом есть просторные офисы с зонами коворкинга; — Возможность прокачаться во всех интересующих направлениях: стать тимлидом, архитектором, разработчиком full-stack; — Развитая система наставничества, сертификация за счёт компании, участие в конференциях, изучение английского языка; — Активный обмен опытом, внутренние и внешние митапы, хакатоны, доклады по развитию hard и soft skills; — 50+ клубов по интересам и профессиональных сообществ внутри компании; — Корпоративная база знаний и социальная сеть для удобства общения с каждым из 1500+ сотрудников. Дополнительно: — Решение Министерства цифрового развития, связи и массовых коммуникаций РФ о предоставлении государственной аккредитации организации, осуществляющей деятельность в области информационных технологий от 10.03.2022 №АО-20220309-3771014210-3

Требуется разработчик с опытом работы на Java и Python для участия в проекте, направленном на создание платформы для обработки юридических документов и корпоративных данных. В рамках проекта применяются технологии LLM-агентов, RAG-поиска, MCP-инструментов, а также интеграция с внутренним веб-сервисом, построенным на Java/Spring. Основные обязанности включают разработку backend-сервисов на Python и Java, интеграцию LLM, внешних API и MCP-инструментов, реализацию RAG, embeddings и поисковых функций по документам, создание механизмов проверки результатов моделей, работу с PostgreSQL, Redis и файловыми хранилищами, написание тестов, настройку логирования и мониторинга, а также участие в проектировании решений и выводе функционала в production. Кандидат должен уверенно владеть Python и Java, иметь опыт разработки backend-сервисов и REST API, практические знания LLM или понимание принципов LLM-приложений, опыт работы с PostgreSQL, умение писать тестируемый код и разбираться в существующем коде, а также понимание вопросов безопасности и разграничения доступа к данным. Важна самостоятельность в решении задач и способность взаимодействовать с другими командами. Дополнительным преимуществом будет опыт работы с RAG, embeddings, семантическим или гибридным поиском, разработкой MCP-сервисов или LLM-agent систем, обработкой DOCX, PDF и Excel с использованием OOXML или Apache POI, оценкой качества ответов LLM, опытом в LegalTech, ContractTech или электронном документообороте, а также знание корпоративных или локально развёрнутых LLM. Проект рассчитан до конца 2026 года с возможным продлением на 2027 год, работа удалённая, зарплата от 200 000 рублей на руки.