Вакансии

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.

Проект: Развиваем платформу для работы с юридическими документами и корпоративными данными. В проекте используются LLM-агент, RAG-поиск, MCP-инструменты и интеграция с внутренним веб-сервисом на Java/Spring. Продолжительность проекта: до конца 2026, плюс в планах пролонгация на 27г. Задачи: — Разработка backend-сервисов на Python и Java. — Интеграция LLM, внешних API и MCP-инструментов. — Реализация RAG, embeddings и поиска по документам. — Создание проверок результатов работы модели. — Интеграция с PostgreSQL, Redis и файловым хранилищем. — Написание тестов, настройка логирования и мониторинга. — Участие в проектировании решений и выводе функциональности в production. Требования: — Уверенное владение Python и Java (важно, чтобы кандидат занимался задачами AI на питоне и мог подключать их к решению на джаве). — Опыт разработки backend-сервисов и REST API. — Практический опыт работы с LLM или понимание основных принципов LLM-приложений. — Опыт работы с PostgreSQL. — Умение писать тестируемый код и разбираться в существующей кодовой базе. — Понимание вопросов безопасности и разграничения доступа к данным. — Готовность самостоятельно решать задачи и взаимодействовать с другими командами. Будет плюсом: — Опыт работы с RAG, embeddings, семантическим или гибридным поиском. — Опыт разработки MCP-сервисов или LLM-agent систем. — Работа с DOCX, PDF и Excel, знание OOXML или Apache POI. — Опыт оценки качества ответов LLM. — Опыт в LegalTech, ContractTech или электронном документообороте. — Знание корпоративных или локально развёрнутых LLM. Обязателен неймчек по образцу: Иванов Иван, Уровень - Senior, 1234@mail.ru (не корпоративная), Локация - Россия, Москва, Часовой пояс - (Мск), Опыт работы - 4+ лет.

Location: Hanoi; Shenzhen / Dongguan / Guangzhou / Foshan Workplace: onsite Ajax Systems, an international tech company and the largest manufacturer of security systems in Europe. We are looking for an experienced Lead CCTV Industrialization Engineer and NPI Expert with broad hands-on expertise in electronic, mechanical, and optical product development, manufacturing introduction, validation, and production testing. The key objective of this role is to share practical expertise across R&D, NPI, and Manufacturing, to build up proper CCTV manufacturing processes, ecosystem, and lines to maximize throughput and efficiency. A mandatory requirement is substantial practical experience in a mature, well-established company with proven engineering and manufacturing processes for video surveillance IP cameras and similar devices. Mandatory requirements 7+ years of practical experience in Video/CCTV device development, NPI, Product Industrialization, Manufacturing Engineering, Product Engineering, or related hardware manufacturing roles Experience in building/managing CCTV manufacturing line Proven hands-on experience across the full product lifecycle in a mature manufacturing company Ability to identify industrialization risks during the design stages and provide practical recommendations to R&D, Mechanical, Hardware, Firmware, QA, and Manufacturing teams throughout all development and manufacturing stages Practical experience with factory production and mass-production ramp-up, including onsite factory support Strong understanding of production-line structure and manufacturing processes for video devices Deep understanding of environmental and reliability validation, including temperature and humidity testing, mechanical testing, and burn-in testing Expert knowledge of optical subsystems, image sensors, and ISP performance, including validation, testing flows, troubleshooting, and issue resolution Ability to identify whether image-quality issues originate from hardware, optics, mechanics, ISP tuning, firmware, or manufacturing processes Practical experience with image sensor contamination control, clean assembly procedures, dust inspection, and dust-ingress prevention Ability to act as a technical authority and knowledge-sharing expert rather than only as an execution-focused engineer Technical Background and Expertise Strong understanding of optics and camera assembly, lens systems, focusing, Active Alignment (AA), dust contamination control, and dust protection Experience with flat protective glass, domes, plastic windows, IR windows, coatings, mechanical stress, and contamination effects on optical systems Clear understanding of plastic and metal enclosure design principles, injection-molded and die-cast part manufacturing technologies, including sealed enclosures, membranes, waterproofing, and other outdoor product design specifics Experience with IPX6 / IPX7-type testing, leak testing, and pressure testing Deep understanding of moisture and condensation issues in sealed devices, including the use of silica gel or molecular sieves and membrane vents Strong practical understanding of video camera manufacturing issues such as damaged pixels, shading, fixed-pattern noise, sensor tilt, and sensor mounting defects Understanding of AWB, AE, OBC, and AI-enabled ISP technologies Experience with factory sensor and optical calibration procedures and image quality validation principles Good understanding of Ethernet, IEEE 802.3af / 802.3at, PoE, and EMI / EMC interactions Working knowledge of Embedded Linux / RTOS, boot processes, factory firmware flashing, Secure Boot, OTA updates, and recovery mechanisms Basic practical understanding of 2.4 GHz / 5 GHz behavior, interference, attenuation, antenna placement, and enclosure impact Understanding of Li-ion / LiFePO4 fundamentals, charging and discharging, standby and active current consumption, and realistic battery-life validation Basic understanding of microphone and speaker validation, including enclosure and waterproof membrane impact Preferred knowledge of video WDR / HDR, AI-enabled ISP technologies, low-light optimization, H.264 / H.265 / AV1 fundamentals, and bitrate / quality trade-offs We offer The opportunity to suggest and implement your own ideas that directly influence the product and its mass production A place in a fast-growing company with a motivated and professional team Competitive salary Medical insurance Corporate English classes The opportunity to participate in Ajax’s internal beta-testing program for security systems Discounts from partner companies Discounts on Ajax products Ajax Systems is a Ukrainian success story that has grown into a global product. Ajax devices are trusted by more than 4.5 million end users and 330,000 PRO users in over 180 countries. The company provides comprehensive solutions to protect residential and commercial properties of all sizes. Today, Ajax portfolio includes 280 devices for intrusion protection, video surveillance, fire safety, and comfort and automation.

Задачи: — Развивать и сопровождать инфраструктуру AI-платформы. — Разворачивать и поддерживать сервисы в Kubernetes. — Контейнеризировать приложения и инфраструктурные компоненты с использованием Docker. — Разрабатывать и поддерживать CI/CD-пайплайны. — Автоматизировать рутинные операции с помощью Python и Bash. — Работать с ArgoCD и IaC-инструментами. — Настраивать мониторинг, логирование и health-checks. — Работать с persistent storage и S3/object storage. — Участвовать в эксплуатации AI/ML-сервисов и LLM inference, администрировать AI системы. — Постепенно осваивать GPU-инфраструктуру и особенности запуска AI-сервисов на GPU. — Участвовать в развитии внутренних стандартов deployment и эксплуатации AI-сервисов. — Разбираться в инцидентах и самостоятельно доводить технические задачи до результата. — Взаимодействовать с архитекторами, разработчиками, специалистами по кибербезопасности, ML-инженерами и системными администраторами. Требования: — Уверенное владение Linux и командной строкой. — Практический опыт с Docker и Docker Compose. — Базовый практический опыт работы с Kubernetes. — Понимание основных Kubernetes-объектов: Pod, Deployment, Service, ConfigMap, Secret, Ingress, PVC. — Опыт работы с Git. — Понимание принципов CI/CD и практический опыт работы хотя бы с одним CI/CD-инструментом. — Bash на уровне написания небольших скриптов автоматизации. — Python на уровне написания automation scripts и небольших сервисных утилит. — Базовое понимание сетей: TCP/IP, DNS, HTTP/HTTPS. — Понимание принципов мониторинга и логирования. — Понимание разницы между файловым, блочным и объектным хранилищем. — Понимание базовых принципов работы AI/ML-сервисов: model, training, inference, embeddings, LLM. — Понимание базовых принципов применения мер защиты от киберугроз. — Будет плюсом: Helm, Ansible, ArgoCD / GitOps, Prometheus / Grafana / Loki, S3 / MinIO, PostgreSQL / Redis, опыт работы с GPU и NVIDIA, NVIDIA Container Toolkit / CUDA, vLLM, SGLang, Triton или другие inference engines, MLflow, Airflow, Kubeflow или другие MLOps-инструменты, RAG, vector databases, embeddings, непосредственный опыт эксплуатации AI/ML или LLM-сервисов, опыт работы в инфраструктурной или DevOps-команде, опыт эксплуатации production-систем, опыт работы с on-premise инфраструктурой, опыт работы с Linux-серверами и Kubernetes-кластерами, понимание принципов отказоустойчивости и масштабирования. — Английский язык на уровне чтения технической документации. — Не требуется глубоких знаний ML и разработки нейронных сетей. — Не требуется самостоятельно обучать модели или разрабатывать ML-алгоритмы. — Важнее хорошая инженерная база, способность самостоятельно разбираться в проблемах и готовность постепенно углубляться в AI infrastructure. Условия: — Стабильный и прозрачный доход: размер заработной платы обсуждается по итогам собеседования + премия по результатам KPI. — Сложные и интересные задачи, современный стек технологий. — Забота о здоровье: программа ДМС с первых дней работы, включая стоматологию и обслуживание в лучших клиниках города. — Возможность вертикального и горизонтального карьерного роста: регулярные тренинги, вебинары, митапы и демо-дни. — Корпоративные предложения от партнёров группы АЛЬФА-БАНК. — Бесплатная мобильная связь Альфа-Мобайл.

Команда «Мотивационный профиль» создает новый подход к работе с клиентом, который позволит перейти от классической персонализации к более глубокому пониманию человека на основе психологии. Мы извлекаем из данных психометрические характеристики — ценности, страхи, особенности характера и принятия решений — и используем их в продуктах банка и экосистемы. Для построения мотивационного профиля мы используем анкеты, подробные интервью, поведенческие и транзакционные данные. Работаем с известными психометрическими методиками и подходами, разработанными исследовательской командой. Одна из ключевых задач — научиться надежно определять психометрические характеристики на ограниченных выборках, масштабировать их на широкую клиентскую базу и использовать в рекомендательных сценариях и моделировании клиентских реакций. Задачи: — работать с таргетами, полученными из анкет и интервью с помощью экспертной и LLM-разметки — подбирать постановку задачи в зависимости от объема и качества данных: supervised learning, weak supervision, few-shot и другие подходы — сравнивать методы моделирования — от rule-based решений и классического ML до моделей на основе embeddings и LLM — обучать модели для предсказания психометрических и поведенческих характеристик клиента — масштабировать характеристики, доступные для ограниченной выборки клиентов, на широкую клиентскую базу — оценивать модели как по стандартным ML-метрикам, так и по тому, насколько хорошо их результаты работают в прикладных сценариях - сегментации, персонализации и моделировании поведения клиента — работать с неструктурированными данными, в том числе с текстами глубинных интервью — строить пайплайны обработки текста: чанкинг, построение эмбеддингов, кластеризация и извлечение признаков — сопоставлять информацию из текстов с поведенческими и транзакционными данными — проверять качество разметки и данных, анализировать смещения выборки и устойчивость полученных признаков — оценивать репрезентативность обучающей выборки и возможность переноса результатов на широкую клиентскую базу. Требования: — опыт работы Data Scientist / ML Engineer от 2–3 лет — уверенное знание Python и стандартного DS/ML-стека: pandas, numpy, scikit-learn, CatBoost / LightGBM / XGBoost — опыт построения supervised ML-моделей для табличных данных — хорошее понимание задач классификации, регрессии и ранжирования — опыт работы с текстовыми данными и NLP: embeddings, sentence-transformers, кластеризация, классификация текстов, семантический поиск — понимание основных подходов к работе с LLM: prompting, few-shot, zero-shot, LLM-разметка и оценка качества LLM-ответов — умение проектировать метрики и схемы offline-валидации для нестандартных задач — умение работать с неоднозначными таргетами, шумной разметкой и неполными данными — уверенная работа с большими данными — способность переводить бизнес-гипотезу в ML-задачу: формализовать постановку, определить требования к данным и метрикам, учесть ограничения и сформулировать критерии успешности — умение понятно объяснять результаты как ML-специалистам, так и продуктовой команде. Условия: — комфортный современный офис рядом с м. — Кутузовская — возможность выбрать удобный график – офис/гибрид — ежегодный пересмотр зарплаты, годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

Задачи: — Design and implement high-quality native mobile SDKs that enable developers to integrate embedded wallet functionality into their applications, with a strong focus on API ergonomics, performance, and reliability — Own SDK features end-to-end: from API design and architecture, through implementation and testing, to release, versioning, and developer documentation — Build and maintain secure, reliable SDK components for wallet operations, including cryptographic operations, blockchain interactions, and security-sensitive flows — Define and evolve the SDK architecture to support long-term maintainability, backward compatibility, and adoption across diverse customer applications — Build sample applications and demos that showcase SDK capabilities and serve as integration references for customers — Create clear, comprehensive SDK documentation and integration guides — Work with developer relations and support teams to address customer integration challenges — Raise the bar for quality and operational excellence by establishing best practices around testing, observability, releases, and code health Требования: — 5+ years of software engineering experience, with deep focus on mobile development and shipping production-grade SDKs — Demonstrated track record of building and shipping high-quality code, with work you can point to and discuss in depth — Strong hands-on experience with iOS (Swift) and/or Android (Kotlin); experience with React Native or other cross-platform frameworks — Experience building mobile SDKs or shared libraries, including API design, versioning, packaging, and long-term maintenance across platforms — Experience with blockchain-enabled mobile systems, including wallet flows, signing, and on-chain interactions — Experience working on security-sensitive mobile software, such as key management, secure storage, cryptography, or financial products — Strong grasp of mobile engineering best practices, including CI/CD, automated testing, release management, and observability Условия: — Remote role in a distributed team; preference for candidates in the Americas time zone

Задачи: — Готовить данные (API, SQL, файлы) и собирать прототипы для AI-ассистента до передачи в продакшен. — Подготавливать контекст для RAG, тестировать поиск и сопоставление сущностей. — Формировать golden dataset и автоматизировать тестирование ассистента (массовый прогон сценариев). — Считать метрики качества (retrieval, hallucinations) и локализовывать причины ошибок. Требования: — 5+ лет опыта в Data Engineering / разработке аналитических решений. — Уверенный Python и SQL, опыт с API, ETL/ELT, DWH. — Понимание принципов LLM, RAG, retrieval, embeddings (опыт работы — жирный плюс, но если глубоко погружен в тему — welcome). — Умение быстро собирать PoC, исследовать источники и находить нестандартные решения. Условия: — Удалёнка из любого города РФ или офис по желанию. — Оформление по договору ИП. — Зарплата 350 000 – 400 000 ₽ Gross.

Задачи: — Разрабатывать и улучшать RAG-приложения: chunking, embeddings, retrieval и reranking. — Строить LLM-воркфлоу и агентов с использованием LangChain, LangGraph, LlamaIndex, CrewAI или аналогичных. — Разрабатывать переписывание запросов, классификацию намерений, маршрутизацию и структурированное извлечение. — Интегрировать LLM API/модели в производственные системы. — Работать с OpenAI, Gemini, Hugging Face и PyTorch. — Вносить вклад в SFT, LoRA, QLoRA, PEFT и оценку LLM. — Работать с OCR, PDF/DOCX, STT и памятью для диалогов. — Создавать API с Python / FastAPI и работать с Docker, Git, тестированием и CI/CD. — Мониторить и улучшать качество моделей, задержки и стоимость. — Брать ответственность за отдельные функции/компоненты под наставничеством старшего инженера. Требования: — 2–4 года опыта в NLP / ML / Information Retrieval / Applied AI. — Сильные навыки Python. — Практический опыт с LLM / RAG / AI-agent приложениями. — Понимание трансформеров, embeddings, prompting, retrieval, chunking и reranking. — Опыт с LangChain / LangGraph / LlamaIndex / CrewAI. — Опыт с Qdrant / Pinecone / Milvus / Weaviate / Elasticsearch. — Опыт с OpenAI / Gemini / Hugging Face и знание PyTorch. — Понимание оценки LLM и снижения галлюцинаций. — Навыки FastAPI, REST, Docker, Git. — Многоязычный NLP будет плюсом. — Профессиональный английский. Желательно: — Знания SFT / LoRA / QLoRA / PEFT. — Знания BM25 / RRF. — Опыт с RAGAS / DeepEval / LangSmith / Langfuse. — Опыт с OCR/STT. — Опыт с Graph RAG. — Опыт с vLLM/TGI/Triton. — Оптимизация под GPU. — Знание корейского языка. Условия: — Работа в Инчхоне, Южная Корея. — Полная занятость. — Формат работы: офис / гибрид. — Конкурентная зарплата, обсуждается индивидуально. — Возможна поддержка с визой и релокацией.

Location: New York, NY (HQ) 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 THE ROLE We’re looking for a Principal Brand Designer to define the brand design system that takes Ramp into new futures. You’ll turn our strategy into design, lead our most important brand projects, and help the team do its best work. This is a leadership individual contributor role. You can take any idea from zero to one. You understand the business fundamentals, the product value, the channels that perform best, and you know where brand design amplifies and improves the work. You understand each XFN’s accountability and outcomes, from product, marketing, GTM, growth and beyond. You can speak their language. WHAT YOU'LL DO - Set creative direction. Turn business goals and audience insights into clear ideas for the brand. - Lead company-defining projects. Take campaigns, launches, and other brand experiences from an open question to finished work. - Set the bar for quality and taste. You are the go-to expert for typography, imagery, layout, and brand design. You are the champion for this standard. - Build the brand system. Evolve our visual identity over time. We believe in coherency over consistency. We built our brand system to behave like jazz. - Connect brand and product. Work with product, marketing, and engineering so the company feels coherent wherever people experience it. - Raise the quality of the team’s work. You are a player-coach that can mentor others and develop a culture of collaboration, creativity, and trust. - Tell stories to drive alignment. Help partners understand the thinking, agree on a direction, and make practical choices about scope and timing. WHAT MAKES YOU STAND OUT - You’re already using AI tooling and agents to explore faster, iterate sooner, and develop ideas that you couldn’t before. - You have both agency and in-house experience. - You have side hobbies exploring design and creativity. We want to see side projects that show a desire for learning, a student-like mindset, and above all, enthusiasm for where the future is headed. - You’re humble, thoughtful, and self-aware. - Your portfolio reveals a story about how you’ve grown as a person, your interests, and curiosities. - You understand that execution is how good ideas win. WHAT YOU’LL BRING - A deep portfolio of brand identities, marketing campaigns, bringing brand into product experiences, and digital applications, with a clear explanation of your contribution. - Excellent design fundamentals and the ability to carry an idea through to finished work. - Experience leading complex projects independently and making good decisions when the brief is still taking shape. - The ability to negotiate, compromise, or understand trade-offs with executives and XFN partners. - The ability to connect creative choices to the audience, the product, and the business. - A record of helping other designers improve and working well with people across disciplines. 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: San Francisco, CA 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 Hardware is becoming critical to how AI gets built, and the legal questions are as new as the technology. We're hiring a Commercial Counsel, Hardware to help Anthropic's product teams move fast, build safely, and get the legal calls right. In this role, you'll negotiate a high volume of fast-moving deals for hardware, components, software, and emerging technology procurement needs. This role will also lead multifaceted technology, data, and partnership deals, many of them the first of their kind at Anthropic. You'll be the legal and risk partner that technical and business leaders turn to when they need a fast, practical read on a deal or purchase. You'll clear low risk transactions quickly, spot the ones that deserve a closer look, and build scalable solutions as new complexities come up, all while working alongside Procurement and Finance and Legal colleagues in areas like privacy, IP, and trade. Along the way, you'll also support partnerships with major tech companies, universities, and media organizations, which gives you a view across much of the business. Key responsibilities Negotiate and close a high volume of agreements for hardware and components, including review of embedded firmware and companion software licenses Structure and negotiate complex technology, data, and partnership agreements Track legal and regulatory changes that affect hardware products and components, such as product safety rules and restrictions on foreign-made components, and explain what they mean for our plans Identify trade, export control, sanctions, and supply chain concerns in cross-border sourcing, and work with our national security and compliance teams to resolve them Build and maintain self-serve guidance, templates, and AI-assisted review tools so routine purchases don't wait on Legal Manage outside counsel for specialist questions and overflow volume Cover other technology or hardware transactions on the Legal team as needed Minimum qualifications Hold a law degree (JD, LLB, or equivalent) and meet the local licensing requirements to work as in-house counsel where you'll be based Hands-on experience buying hardware and firmware, components, and software in high volume, fast-paced environments Deep experience leading complex deals, such as technology or data licensing, joint development, or supply partnerships Working knowledge of international supply chains and how sourcing choices (and their associated risks) show up in contracts (lead times, single-source risk, warranties, country of origin, higher risk jurisdictions) Working knowledge of trade and regulatory compliance for hardware, including export controls, sanctions, tariffs and customs Clear, plain communication with scientists, engineers, and operators, so legal risk turns into a decision they can act on A low-ego, "doer" attitude and real interest in taking on work outside your core practice area Intense curiosity about AI and emerging hardware, and a commitment to responsible AI development Preferred qualifications At least 8 years of legal experience in hardware or a closely related technical space, with the judgment to run risk assessments and deals on your own from day one In-house time at a hardware-intensive technology company (such as electronics, industrial automation, or complex devices) while it was scaling quickly Experience supporting product or engineering teams as a dedicated counsel Experience with facilities or on-site vendor services, including the insurance and safety questions An engineering, product, or other technical background Experience using AI tools to speed up your own legal work Role-specific policy: For this role, we expect all staff to be able to work from our San Francisco office at least 3 days a week, though we encourage you to apply even if you might need some flexibility for an interim period of time. 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: $335,000 — $385,000 USD 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.