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Location: Dublin, Ireland Fin , now part of Salesforce, is on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk, giving modern support teams one single system. Together with Salesforce, the #1 AI CRM, where humans with agents drive customer success, we're building the future of customer experience. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword, it's a way of life. The world of work as we know it is changing, and we're looking for Trailblazers who are passionate about bettering business and the world through AI. Ready to level up your career at the company leading workforce transformation in the agentic era? You're in the right place. Agentforce is the future of AI, and you are the future of Salesforce. What's the opportunity? We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Fin's next generation of AI products. Fin is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month. You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks, and is the foundation of the AI Group's full stack approach to AI. We’re particularly interested in engineers who have: A track record of working on model training or model inference at scale , or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better. What will I be doing? As a Senior AI Infrastructure Engineer focused on model training and inference, you will: Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation. Build and optimize inference services that deliver low‑latency, high‑reliability experiences for our customers, including autoscaling, routing, and fallbacks. Work on GPU‑level performance : tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack. Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production. Play an active role in hiring, mentoring, and developing other engineers on the team. Raise the bar for technical standards, reliability, and operational excellence across Fin's AI platform. Profile we’re looking for: These are indicative, not hard requirements We’re looking to hire Senior+ AI Infrastructure Engineers . You’re likely a great fit if: You have 5+ years of experience in software engineering , with a strong track record of shipping high‑quality products or platforms. You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals). You have hands‑on experience with one or more of the following: Model training (especially transformers and LLMs). Model inference at scale (again, especially transformers and LLMs). Low‑level GPU work , such as writing CUDA or Triton kernels. Comfortable working in production environments at meaningful scale (traffic, data, or organizational). You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers. You take pride in strong technical fundamentals , love learning, and are willing to invest in your own development. Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly. Bonus skills & attributes None of these are required, but they’re nice to have: Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies). Experience running training or inference workloads on Kubernetes . Experience with AWS or other major cloud providers. Production experience with Python in ML or infrastructure contexts. Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings #LI-Hybrid Unleash Your Potential When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world. In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. At Fin, we want to give people what they need to do the best work of their careers. Our benefits and programs are designed to support your health and wellbeing, your family, your time away from work, and your financial future. Offerings vary by location in line with local practices and requirements. Learn more about working at Fin and the benefits we offer at fin.ai/careers . Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. Policies Fin has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week. We have a radically open and accepting culture at Fin. We avoid spending time on divisive subjects to foster a safe and cohesive work environment for everyone. As an organization, our policy is to not advocate on behalf of the company or our employees on any social or political topics out of our internal or external communications. We respect personal opinion and expression on these topics on personal social platforms on personal time, and do not challenge or confront anyone for their views on non-work related topics. Our goal is to focus on doing incredible work to achieve our goals and unite the company through our core values . Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

Задачи: — Разработка инструментов автоматизации процессов сборки, тестирования, внедрения — Построение пайплайнов разной сложности — Разработка сервисов интеграции с помощью брокеров сообщений и HTTP запросов — Разработка web-сервисов для пользователей — Поддержка созданных и текущих инструментов и сервисов Требования: — Опыт от 3 лет в DevOps / Platform / Tools development — Опыт работы с Kubernetes (деплой, Helm, базовый troubleshooting) — Опыт разработки на Python (коммерческий, не только скрипты) — Опыт написания Jenkins-пайплайнов (Groovy) — Уверенное знание Linux / bash — Опыт с Docker и Git — Понимание работы Nginx — Понимание брокеров сообщений (Kafka будет плюсом) — Готовность участвовать в разработке веб-сервисов (REST API) — Готовность поддерживать созданные инструменты и сервисы Будет плюсом: — Опыт с OpenShift — Опыт с Go или Java — Опыт с ArgoCD / GitOps — Опыт с Istio — Опыт работы с Jenkins Условия: — Полностью удалённый формат работы (только РФ) — Работа в крупном и стабильном финансовом холдинге — Возможность участвовать в развитии и поддержке сложных инфраструктурных решений

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a Senior Dev/MLOps Engineer to join our Data & Compute Platform team based in London. WHAT YOU’LL DO - Own and manage the full lifecycle of both ML models and core infrastructure - from development and deployment to monitoring and continuous improvement. - Build and maintain robust CI/CD pipelines for both software and ML workflows. - Ensure reliability, scalability, observability, and security of production systems and ML infrastructure. - Automate deployment, orchestration, and environment management using modern DevOps tooling. - Collaborate closely with software engineers, data scientists, and product teams to bring ML-powered features to production. - Proactively detect, troubleshoot, and resolve infrastructure and model performance issues. - Stay up to date with industry best practices in DevOps, MLOps, and infrastructure engineering. - Document infrastructure, workflows, and operational procedures clearly and thoroughly. WHAT WE'RE LOOKING FOR - Proven experience in a senior-level DevOps, MLOps, or related infrastructure-focused engineering role. - Strong proficiency in Python and familiarity with ML frameworks such as TensorFlow or PyTorch. - Deep experience with cloud platforms (AWS, GCP, or Azure) and container orchestration tools (Docker, Kubernetes). - Solid understanding of CI/CD systems (e.g., GitHub Actions, GitLab CI, ArgoCD) and infrastructure-as-code tools (e.g., Terraform, Helm). - Familiarity with data engineering concepts such as ETL pipelines, data lakes, and large-scale batch/stream processing. - Ability to design scalable, secure, and observable systems in fast-moving environments. - Strong debugging and problem-solving skills across distributed systems. - Excellent collaboration and communication skills, with experience working in cross-functional teams. - Understanding of security and compliance best practices for both software and ML systems. WHAT WE OFFER - Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays. - Fully funded private healthcare for UK employees, with broad provider access, virtual and in‑person care, and strong mental health and serious illness support. - Equity included–we believe builders should share in what they build. - Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in‑office. - Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics. - Freedom to influence the product and own key initiatives.

Задачи: — Поддержка и развитие функционала образовательной платформы — Разработка архитектуры проекта — Интеграция с внешними сервисами — Рефакторинг Требования: — Опыт коммерческой разработки от 7 лет с Node.js, React.js, TypeScript, PostgreSQL — Опыт работы в роли ведущего, Lead разработчика — Уверенный опыт работы с базами данных, оптимизация сложных запросов — Уверенные навыки работы с инфраструктурой Docker, Kubernetes — Усидчивость, умение предлагать решения, многозадачность Условия: — Оформление по ТК РФ — Удалённый формат работы на территории РФ с полной занятостью — Возможность быстрого профессионального развития

Задачи: — Участие в проектировании, разработке и поддержке сервисов/микросервисов. — Code review в команде. — Разработка юнит-тестов и тестирование собственного кода на соответствие критериям приемки. — Анализ инцидентов. — Активное участие в формировании DevOps-решений, инструментов и автоматизации. — Разработка и внедрение CI/CD, интеграция с сервисами, настройка сервисов мониторинга и уведомлений, организация автоматизированного тестирования и проверки кода. Требования: — Опыт создания production-решений, простых в эксплуатации (способных работать 24х7, с развернутой системой метрик prometheus/grafana/ELK). — Глубокое знание и понимание архитектурных принципов. — Углубленное знакомство с Kubernetes, включая написание операторов и контроллеров. — Отличное владение Python, умение работать с асинхронным кодом. — Знание классических алгоритмов и структур данных. — Навыки технического лидерства проектом. — Опыт работы с сервисами под большой нагрузкой. — Навыки работы с Linux. — Опыт работы с реляционными базами данных.

Location: Hong Kong, Hong Kong SAR 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 You will be responsible for the core architecture design and technical delivery of an enterprise-grade AI Native engineering platform, driving the deep integration of large language models, AI Agents, knowledge capabilities, and the software development lifecycle. The platform will support intelligent capabilities across requirement analysis, solution generation, coding collaboration, test automation, and environment orchestration.This role focuses on engineering-grade AI implementation in complex R&D scenarios. You are expected to turn model capabilities into reusable, scalable, and governable platform capabilities, and continuously drive the evolution of the engineering platform toward an AI Native architecture. What You’ll Be Doing Lead the overall architecture design and core capability development of the AI Agent platform, including task orchestration, tool invocation, context management, memory mechanisms, and multi-agent collaboration. Drive the implementation of AI Native capabilities in engineering workflows , including AI coding, technical solution generation, test case generation, test report generation, and automated environment deployment. Build the knowledge augmentation layer for the platform, including the design, integration, and application of RAG, GraphRAG, and knowledge graph capabilities. Establish the runtime and governance framework for AI Agents, including execution isolation, permission control, auditability, evaluation, stability, and cost governance. Collaborate with product, engineering, QA, and operations teams to standardize, productionize, and scale platform capabilities, continuously improving engineering efficiency and delivery quality. What We Look For In You Bachelor’s degree or above , preferably in Computer Science, Software Engineering, Artificial Intelligence, or related fields. 8+ years of experience in backend engineering, platform engineering, or infrastructure engineering, with strong system design and delivery capabilities. Proficient in Java / Python , with solid coding skills, architectural thinking, and experience building complex systems. Strong understanding of LLM and AI Agent architectures , including task decomposition, tool use, context management, memory, execution loops, and exception handling. Hands-on experience with LangChain and LangGraph , and strong familiarity with mainstream AI coding tools or frameworks such as Claude Code , Codex, Cursor , and Copilot . Experience in designing or developing RAG / GraphRAG / knowledge graph solutions for enterprise knowledge augmentation and engineering use cases. Good understanding of DevOps , CI/CD , Kubernetes , and cloud-native technologies , with strong cross-functional collaboration and execution skills. Nice to Haves Experience contributing to open-source projects related to AI Agents, AI Coding, RAG, or knowledge graphs. Experience building enterprise engineering platforms, DevOps platforms, or AI platforms . Familiarity with open-source Agent projects such as openclaw, openhands/openharness, or Hermes . 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 dependants More that we love to tell you along the process! OKX Statement: OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Discl aimer: Please note that Hong Kong is a group-level service hub, and OKX does not carry on a business of operating a virtual asset trading platform in Hong Kong. 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: Yerevan; Poland; Ukraine; Serbia; Valencia Workplace: remote About Provectus Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.   Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.   Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDX; our delivery arc is Sprint → Enable → Realize: Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction. Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered. Requirements: Mindset Proactive and self-directed; identify problems before they're handed to you Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job B2+ English, comfortable collaborating across distributed, multicultural teams Client Engagement You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run Technical depth 7+ years building and running production systems. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Experience in making and defending architectural trade-off decisions Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release Model and agent monitoring, drift detection Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus What you’ll do: Take the seat Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow. Build Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team. Own the outcome. Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here. Be credible with the customer’s engineers and their executives. Shape what we commit to before we commit to it. Nice to Have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution Experience in one of the industries: financial services, insurance, healthcare Consulting, professional services, or other embedded customer-facing delivery A2A: you can explain agent-to-agent interoperability AWS and Claude Code Certifications CI/CD pipeline experience (GitHub Actions, GitLab CI) Experience in an additional language (Go, TypeScript, or Rust) Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire:   Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant  HR Interview. Soft skills and expectations HM interview. Tech questions; a live engineering session is also possible

Location: Raleigh, NC Workplace: Remote ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers - Occasionally contribute code upstream when it meaningfully improves customer outcomes WHAT YOU’LL BRING - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S: - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability, or guardrails - Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts - Have shipped and operated production software and are comfortable owning systems under real-world constraints COMPENSATION Annual OTE range: $150,000–$250,000 USD COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: Charlotte, NC Workplace: Remote ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers - Occasionally contribute code upstream when it meaningfully improves customer outcomes WHAT YOU’LL BRING - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S: - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability, or guardrails - Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts - Have shipped and operated production software and are comfortable owning systems under real-world constraints COMPENSATION Annual OTE range: $150,000–$250,000 USD COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Продукт: Торговое ядро криптоплатформы О РОЛИ Мы ищем Senior-разработчика для развития высоконагруженного торгового ядра. Ваша работа будет сосредоточена на проектировании сервисов маршрутизации ордеров, риск-менеджмента и систем с ультранизкими задержками. Это позиция для инженеров, готовых отвечать за стабильность критически важных финансовых систем. ОСНОВНЫЕ ЗАДАЧИ — Проектирование масштабируемых сервисов (ордера, балансы, расчет рисков). — Оптимизация производительности для достижения сверхнизких задержек (Low-latency). — Обеспечение отказоустойчивости: внедрение систем самовосстановления и Graceful Degradation. — Усиление безопасности API, реализация Rate-limiting и защиты торговых операций. — Контроль качества через нагрузочное тестирование и соблюдение строгих SLA. ТРЕБОВАНИЯ К КАНДИДАТУ — 5+ лет коммерческого опыта разработки на стеке .NET/C#. — Обязателен опыт в разработке криптобирж, торговых платформ или биржевых продуктов. — Глубокие знания конкурентного программирования и Event-driven архитектуры. — Опыт работы с брокерами сообщений (Kafka, NATS или RabbitMQ). — Понимание принципов безопасности API, аудита и целостности данных. БУДЕТ ПРЕИМУЩЕСТВОМ — Опыт работы с FIX-протоколом, WebSockets и торговыми API. — Знание Cloud-native стека (Kubernetes, AWS/GCP, Service Mesh). — Опыт разработки критически важных систем в традиционном финтехе. УСЛОВИЯ РАБОТЫ — Сложные инженерные вызовы и работа в команде экспертов индустрии. — Полностью удаленный формат и гибкий график. — Поддержка профессионального развития и участия в профильных конференциях. — Возможность прямого влияния на архитектуру и успех глобального продукта.

Задачи: — Участие в разработке и деплое ML/LLM решений. — Применение Prompt Engineering и методов Fine-tuning/PEFT. — Построение RAG пайплайнов и работа с векторными базами данных. — Оптимизация производительности LLM и оценка их качества. — Разработка с использованием мультимодальных моделей. Требования: — Уверенное владение Python. — Опыт деплоя ML/LLM решений с использованием Docker, Kubernetes, облачных платформ AWS, GCP, Azure. — Опыт работы с Prompt Engineering (few-shot, chain-of-thought, ReAct). — Знание Fine-tuning / PEFT (LoRA, QLoRA, adapters). — Опыт построения RAG пайплайнов (chunking → embedding → retrieval → генерация). — Умение работать с векторными базами данных (Qdrant, Pinecone, Milvus, FAISS). — Опыт RLHF или работы с синтетическими данными (предпочтения, генерация и фильтрация). — Знание фреймворков LangChain, LlamaIndex, Hugging Face. — Опыт оценки LLM (BLEU, ROUGE, MMLU, кастомные эвалы). — Опыт оптимизации инференса (vLLM, TensorRT, ONNX). — Опыт работы с мультимодальными моделями (LLaVA, GPT-4V, Qwen-VL). — Грейд Middle/Middle+. Условия: — Полная занятость. — Удалённый формат работы. — Интересные проекты в области ИИ.

Location: Dublin, Ireland; Galway, Ireland; Leeds, England, United Kingdom; London, England, United Kingdom About Us Fanatics is a leading global sports platform redefining how fans connect with the teams, players and moments they love. Our businesses — Fanatics Commerce, Fanatics Collectibles, Fanatics Betting & Gaming, Fanatics Events, Fanatics Advertising, Fanatics Markets and Fanatics Studios — maximize the presence and reach for hundreds of partners globally and create a one-stop experience that allows fans to buy, bet, collect, and earn, while engaging with sports in deeper, more immersive ways. This includes licensed fan gear and lifestyle products, sportsbook and iGaming, physical and digital trading cards, live events, premium sports entertainment content, credit card offerings and more. Together, they form a powerful, integrated ecosystem — connected by the Fanatics ONE loyalty program and FanCash, the currency of sport — delivering personalized experiences to over 100 million fans worldwide. Fanatics holds partnerships with over 900 sports properties, from professional leagues and players’ associations to teams, colleges and retail partners; over 7,000 athletes and celebrities; and over 2,000 retail locations, including all Lids stores. Backed by a team of more than 22,000 employees, Fanatics is committed to relentlessly enhancing the fan experience. About the Team Launched in 2021, Fanatics Betting and Gaming is the online and retail sports betting subsidiary of Fanatics, a global digital sports platform. The Fanatics Sportsbook is available to 95% of the addressable online sports bettor market in the U.S. Fanatics Casino is currently available online in Michigan, New Jersey, Pennsylvania and West Virginia. Fanatics Betting and Gaming operates twenty-two retail sports betting locations, including the only sportsbook inside an NFL stadium at Northwest Stadium. Fanatics Betting and Gaming is headquartered in New York with offices in Denver, Leeds and Dublin. Overview Fanatics Betting & Gaming (FBG) is seeking a Staff Platform Engineer to join our Platform Engineering team based in the UK or Ireland. This position plays a critical role within the EMEA Platform Engineering organisation, partnering closely with teams across the UK, Ireland, and United States to build and operate the platforms that power Fanatics Betting & Gaming’s global software ecosystem. As a Staff Engineer, you will operate with a high degree of autonomy and ownership, leading complex technical initiatives across cloud infrastructure, Kubernetes platforms, CI/CD systems, and internal developer tooling. You will influence platform architecture, define standards and best practices, and act as a force multiplier for engineering teams by improving reliability, scalability, and developer productivity. What You’ll Do Help drive technical design and delivery of core platform capabilities spanning cloud infrastructure, Kubernetes platforms, Observability, CI/CD systems, and developer tooling Architect, build, and operate cloud-native platforms on AWS with a focus on reliability, security, scalability, and cost efficiency Lead the evolution of CI/CD pipelines and GitOps-based workflows. Build and maintain internal platform services and tooling that improve developer experience and delivery velocity Continue to drive Infrastructure as Code and automation practices across teams Partner with application teams to unblock delivery and align platform capabilities with developer needs Participate in and/or lead complex, cross-team initiatives and provide technical clarity in ambiguous problem spaces Improve operational excellence through observability, automation, and incident response. Mentor engineers and participate in on-call rotations What You Bring 8+ years of experience in platform, infrastructure, DevOps, or software engineering Proficiency with Python and Java. Familiarity with Kotlin, Go a plus Deep Kubernetes and container orchestration knowledge Proven CI/CD and GitOps experience Infrastructure as Code expertise (Terraform) Strong AWS and cloud-native experience Experience operating distributed, high-availability systems Ability to influence without authority and communicate effectively across regions Nice to Have Experience with internal developer platforms or portals FinOps or cloud cost management experience Regulated or high-availability environment experience Backend development experience Familiarity with AI-assisted development tools Why Platform Engineering at FBG Platform Engineering at FBG operates with a product-first mindset. Our mission is to empower developers with reliable, scalable, and intuitive platforms that accelerate innovation and delivery. Working Across Regions This role operates in a globally distributed environment, working closely with teams across the UK, Ireland, and the United States. Strong communication, time zone coordination, and shared ownership are essential for success. Not sure if you check every box? We believe great engineers come from all kinds of backgrounds. We value impact, technical leadership, and a growth mindset just as much as specific tool experience. Unfortunately, we do not offer sponsorship and you do need to be living in the UK or Ireland to apply. Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process. For information about our benefits, please visit https://benefitsatfanatics.com/ By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy. Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here .

О ПРОЕКТЕ Ocean AI развивает сложные высоконагруженные продукты, интегрирующие блокчейн-технологии и искусственный интеллект. Мы ищем инженера для построения надежной платформенной основы, способной выдерживать специфические нагрузки Web3-нод и AI-моделей. ОСНОВНЫЕ ЗАДАЧИ — Развитие и оптимизация облачной инфраструктуры на базе Kubernetes. — Проектирование и поддержка полных циклов CI/CD (от сборки до автоматических откатов). — Обеспечение высокой доступности (HA), масштабирования и глубокой observability систем. — Сопровождение специфической инфраструктуры: Web3 (ноды, RPC, индексеры) и AI (LLM, RAG). — Внедрение и контроль стандартов безопасности (Secrets management, SAST/DAST). ТРЕБОВАНИЯ К КАНДИДАТУ — 6+ лет опыта в эксплуатации высоконагруженных систем. — Глубокие знания Kubernetes, Helm и IaC (Terraform / Ansible). — Опыт работы с современным CI/CD стеком (GitLab, GitHub Actions, ArgoCD). — Владение инструментами Observability (Prometheus, Grafana, логирование и трассировка). — Опыт администрирования PostgreSQL, Redis, Kafka и работы с облаками (AWS/GCP/Azure). — Понимание сетевых протоколов (TCP/IP, DNS, TLS) и системного администрирования Linux. ОБЯЗАТЕЛЬНЫЙ СПЕЦИФИЧЕСКИЙ ОПЫТ — Web3: практическая работа с нодами, ключами и on/off-chain синхронизацией. — AI: опыт вывода LLM/RAG в продакшен, оптимизация latency и стоимости ресурсов. — Security: обеспечение безопасности цепочек поставок и управления секретами. УСЛОВИЯ РАБОТЫ — Полностью удаленный формат из любой точки мира. — Гибкий подход к юридическому оформлению сотрудничества. — Прямое влияние на архитектуру платформы в динамично растущей компании.

Location: Espoo Workplace: Hybrid Role highlights: - Engineering Lead - Orbit Determination - Location: Espoo - Department: Spacecraft Analysis, Orbit and Attitude Control / Engineering - Reports to: VP of Engineering - Employment type: Permanent - Workplace model: Hybrid - Employment is subject to applicable security screening (incl. SUPO, where required) Why this role matters: As an Engineering Lead - Orbit Determination, you’ll lead a small (4-10 people) multidisciplinary team responsible for delivering accurate and reliable orbit determination capabilities that underpin ICEYE’s satellite operations. Your work ensures timely and precise satellite position data, enabling mission-critical systems such as image acquisition, planning, ground station communications, and on-orbit collision avoidance. Who We Are ICEYE delivers space-based intelligence, surveillance, and reconnaissance (ISR) capabilities to governments and allied nations. This includes sovereign and turnkey ISR missions leveraging ICEYE’s world-leading synthetic aperture radar (SAR) satellite technology, as well as access to data from the world’s largest SAR satellite constellation. These capabilities enable partners to detect and respond to critical changes anywhere on Earth with unprecedented speed and accuracy – day or night and in any weather, supported by ultra high-resolution imagery and high-frequency revisits. As a trusted partner for defense, intelligence, security, and maritime domain awareness, ICEYE’s near real-time data creates a tactical advantage for mission-critical operations. Designed for dual use, the platform also serves civil protection and commercial users for natural-catastrophe intelligence, insurance, maritime monitoring (including oil-spill detection), and finance, contributing to global security and community resilience. ICEYE is headquartered in Finland and operates globally across Europe, North America, the Middle East, and Asia-Pacific. We have more than 900 employees, united by a shared vision: improving life on Earth by becoming the global source of truth in Earth Observation. Your day-to-day responsibilities - Lead and develop a multidisciplinary team delivering orbit determination software and services - Own delivery, prioritization, and roadmap alignment in collaboration with product and domain stakeholders - Drive continuous improvement of orbit determination capabilities including accuracy, latency, and scalability - Support team growth through mentoring, hiring, and effective workload distribution aligned with individual strengths What we’re looking for Must haves: - Proven experience leading multidisciplinary teams delivering complex software systems - Strong understanding of modern software engineering practices including deployment, observability, and release processes - Experience with cloud and on-prem environments using technologies such as Docker and Kubernetes - Ability to communicate effectively across deep technical domains and scalable software systems - Interest in understanding high-level fundamentals of orbital dynamics to understand constraints they impose on the software system Nice to haves: - Experience working on systems with strong theoretical components such as flight dynamics, state estimation, or data science - Understanding of orbital dynamics - Track record of hiring and developing engineers across different career stages Application Process - Talent Attraction Screening (remote) - Hiring Manager Interview (remote) - Take-home technical task and panel interview (remote) - System thinking workshop (on-site) - Behavioural interview to assess cultural fit (on-site) Working at ICEYE At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win. How We Work (Our Values) Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way. • Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions. • See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others. • Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions. • Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift. • Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins. Benefits Our benefits are designed to support your health and wellbeing, at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process. Our Commitment to Diversity, Equity, and Inclusion We want ICEYE to be a place where people can be themselves and do great work. Different backgrounds and perspectives make us stronger, which is why we work to create an environment where people feel included, respected, and able to speak up. Whatever your background, we want you to bring your authentic self to the table. We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help. Apply now to start your ICEYE journey, and help us continue to make the impossible possible together. Read more about ICEYE and working with us at http://iceye.comiceye.com http://iceye.com.

Location: Singapore, Singapore OKX will be prioritising applicants who have a current right to work in Singapore, and do not require OKX's sponsorship of a visa. 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. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity You will be responsible for the core architecture design and technical delivery of an enterprise-grade AI Native engineering platform, driving the deep integration of large language models, AI Agents, knowledge capabilities, and the software development lifecycle. The platform will support intelligent capabilities across requirement analysis, solution generation, coding collaboration, test automation, and environment orchestration.This role focuses on engineering-grade AI implementation in complex R&D scenarios. You are expected to turn model capabilities into reusable, scalable, and governable platform capabilities, and continuously drive the evolution of the engineering platform toward an AI Native architecture. What You’ll Be Doing Lead the overall architecture design and core capability development of the AI Agent platform, including task orchestration, tool invocation, context management, memory mechanisms, and multi-agent collaboration. Drive the implementation of AI Native capabilities in engineering workflows , including AI coding, technical solution generation, test case generation, test report generation, and automated environment deployment. Build the knowledge augmentation layer for the platform, including the design, integration, and application of RAG, GraphRAG, and knowledge graph capabilities. Establish the runtime and governance framework for AI Agents, including execution isolation, permission control, auditability, evaluation, stability, and cost governance. Collaborate with product, engineering, QA, and operations teams to standardize, productionize, and scale platform capabilities, continuously improving engineering efficiency and delivery quality. What We Look For In You Bachelor’s degree or above , preferably in Computer Science, Software Engineering, Artificial Intelligence, or related fields. 6+ years of experience in backend engineering, platform engineering, or infrastructure engineering, with strong system design and delivery capabilities. Proficient in Java / Python , with solid coding skills, architectural thinking, and experience building complex systems. Strong understanding of LLM and AI Agent architectures , including task decomposition, tool use, context management, memory, execution loops, and exception handling. Hands-on experience with LangChain and LangGraph , and strong familiarity with mainstream AI coding tools or frameworks such as Claude Code , Codex, Cursor , and Copilot . Experience in designing or developing RAG / GraphRAG / knowledge graph solutions for enterprise knowledge augmentation and engineering use cases. Good understanding of DevOps , CI/CD , Kubernetes , and cloud-native technologies , with strong cross-functional collaboration and execution skills. Nice to Haves Experience contributing to open-source projects related to AI Agents, AI Coding, RAG, or knowledge graphs. Experience building enterprise engineering platforms, DevOps platforms, or AI platforms . Familiarity with open-source Agent projects such as openclaw, openhands/openharness, or Hermes . 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 dependants 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: London Workplace: Hybrid We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. RESPONSIBILITIES - Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads - Manage and optimize Slurm-based HPC environments for distributed training of large language models - Develop robust APIs and orchestration systems for both training pipelines and inference services - Implement resource scheduling and job management systems across heterogeneous compute environments - Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure - Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm - Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services - Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands QUALIFICATIONS - Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management - Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization - Experience with deploying and managing distributed training systems at scale - Deep understanding of container orchestration and distributed systems architecture - High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) - Experience managing GPU clusters and optimizing compute resource utilization REQUIRED SKILLS - Expert-level Kubernetes administration and YAML configuration management - Proficiency with Slurm job scheduling, resource management, and cluster configuration - Python and C++ programming with focus on systems and infrastructure automation - Hands-on experience with ML frameworks such as PyTorch in distributed training contexts - Strong understanding of networking, storage, and compute resource management for ML workloads - Experience developing APIs and managing distributed systems for both batch and real-time workloads - Solid debugging and monitoring skills with expertise in observability tools for containerized environments PREFERRED SKILLS - Experience with Kubernetes operators and custom controllers for ML workloads - Advanced Slurm administration including multi-cluster federation and advanced scheduling policies - Familiarity with GPU cluster management and CUDA optimization - Experience with other ML frameworks like TensorFlow or distributed training libraries - Background in HPC environments, parallel computing, and high-performance networking - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads REQUIRED EXPERIENCE - Demonstrated experience managing large-scale Kubernetes deployments in production environments - Proven track record with Slurm cluster administration and HPC workload management - Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure - Experience supporting both long-running training jobs and high-availability inference services - Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Location: London Workplace: Hybrid We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. RESPONSIBILITIES - Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads - Manage and optimize Slurm-based HPC environments for distributed training of large language models - Develop robust APIs and orchestration systems for both training pipelines and inference services - Implement resource scheduling and job management systems across heterogeneous compute environments - Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure - Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm - Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services - Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands QUALIFICATIONS - Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management - Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization - Experience with deploying and managing distributed training systems at scale - Deep understanding of container orchestration and distributed systems architecture - High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) - Experience managing GPU clusters and optimizing compute resource utilization REQUIRED SKILLS - Expert-level Kubernetes administration and YAML configuration management - Proficiency with Slurm job scheduling, resource management, and cluster configuration - Python and C++ programming with focus on systems and infrastructure automation - Hands-on experience with ML frameworks such as PyTorch in distributed training contexts - Strong understanding of networking, storage, and compute resource management for ML workloads - Experience developing APIs and managing distributed systems for both batch and real-time workloads - Solid debugging and monitoring skills with expertise in observability tools for containerized environments PREFERRED SKILLS - Experience with Kubernetes operators and custom controllers for ML workloads - Advanced Slurm administration including multi-cluster federation and advanced scheduling policies - Familiarity with GPU cluster management and CUDA optimization - Experience with other ML frameworks like TensorFlow or distributed training libraries - Background in HPC environments, parallel computing, and high-performance networking - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads REQUIRED EXPERIENCE - Demonstrated experience managing large-scale Kubernetes deployments in production environments - Proven track record with Slurm cluster administration and HPC workload management - Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure - Experience supporting both long-running training jobs and high-availability inference services - Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Location: San Francisco; Palo Alto; New York City We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us. WHAT YOU WILL WORK ON Examples of real work the team does: - New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. - GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. - Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. - Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving. - Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. WHO WE'RE LOOKING FOR - Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. - You understand modern LLM architectures and are able to bring them up reliably in a production environment. - You've built and operated production distributed systems under real load - ideally performance-critical ones. - Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. - You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. - Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. GOOD IF YOU TOUCHED ANY OF - ML compilers and framework internals: PyTorch internals, torch.compile, custom operators. - Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. - Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. - Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. - Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. QUALIFICATIONS - 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. - Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). - Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). - Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).