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Location: Москва Команда управления развития искусственного интеллекта (блок «Развитие клиентского опыта B2C») ищет NLP/LLM-инженера для развития AI-copilot премиальных клиентских менеджеров. Мы переходим от классических NLP-решений к адаптивным мультиагентным системам, способным самостоятельно решать задачи в контуре бизнеса: поиск по базе знаний, суммаризация профиля, извлечение инсайтов из звонков, управление графиком, проактивные подсказки на звонке. Ищем Senior-инженера, который возьмет на себя архитектурную ответственность за решения с LLM под капотом, выстроит процессы оценки качества и будет писать чистый, поддерживаемый код, готовый к интеграции в продакшн выделенной бэкенд-командой. Обязанности * архитектура и разработка: проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы и автономных AI-агентов с использованием планирования, function calling и мультиагентной оркестрации * дообучение и адаптация: SFT/RLHF/DPO, LoRA/QLoRA под специфичные бизнес-задачи. Принимать решения о том, когда дообучение необходимо, а когда достаточно промпт-инжиниринга или RAG * система оценки (Eval): поддерживать культуру качества, внедряя продуктовые и ML-метрики (RAGAS, DeepEval, кастомные LLM-as-a-Judge), автоматизировать регрессионное тестирование, связывать технические метрики с бизнес метриками * качество кода и интеграция: писать чистый, хорошо документированный и тестируемый python-код, который легко интегрируется и поддерживается бэкенд командой. Формировать стандарты разработки в рамках DS экспериментов и проектировать API-контракты для ML-компонент * менторство и влияние: участвовать в код-ревью, формировать технические стандарты команды, декомпозировать бизнес-задачи и предлагать оптимальные ML-решения. Работать напрямую с продуктом и стейкхолдерами. Требования * опыт в DL/NLP от 4 лет включая обучение/дообучение моделей на PyTorch, из них не менее года практического опыта с LLM * практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain/LangGraph * глубокое понимание архитектуры современных LLM, включая механизмы attention, MoE, RoPE, KV-cache, методы alignment (RLHF/DPO), принципы эффективного инференса * сильная инженерная культура: Python (asyncio, pydantic, fastapi), написание чистого тестируемого кода (pytest), опыт разработки асинхронных микросервисов. Понимание Docker/K8s, CI/CD для ML * оценка и мониторинг: уверенное владение инструментами LLMOps (Arize, LangSmith, W&B, Phoenix). Опыт создания кастомных eval-пайплайнов и понимания того, как метрики влияют на продукт * работа с данными: опыт предобработки сложных данных для подготовки датасетов, знание SQL * автономность и коммуникация: Умение переводить неопределённые бизнес-задачи в технический план, аргументированно отстаивать архитектурные решения, работать без микроменеджмента. будет плюсом: * опыт опромышливания AI-агентов и знание принципов LLMOps, включая CI/CD, оркестрацию и управление версиями промптов * опыт интеграции ML-решений в высоконагруженные системы * понимание безопасности LLM-приложений. Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы после испытательного срока * 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: — участвовать в полном цикле разработки AI-агентов и универсальных ассистентов: от идеи и проектирования до запуска в продакшн — проводить эксперименты, тестировать гипотезы и исследовать новые подходы для создания агентских решений — разрабатывать и внедрять LLM-пайплайны, чат-ботов и интеллектуальные системы — обучать и дообучать NLP-модели под конкретные задачи продукта — интегрировать разработанные модели и логику в бизнес-процессы — мониторить качество работы моделей в продакшне, находить узкие места и оперативно вносить улучшения. Требования: — опыт в NLP и понимание технологий: от классических алгоритмов до трансформеров и генеративных моделей (LLM) — практический опыт коммерческой разработки AI-агентов, чат-ботов и LLM-пайплайнов — опыт обучения глубоких нейронных сетей — уверенное владение PyTorch, NumPy, Pandas, а также фреймворками для работы с агентами: LangChain, LangGraph (или их аналогами) — уверенное владение Python, Git, SQL, Bash, Docker. Умение писать чистый, поддерживаемый и тестируемый код — навык перевода бизнес-требований в конкретные технические и алгоритмические решения. Будет плюсом: — понимание принципов микросервисной архитектуры и опыт проектирования REST API — опыт разработки асинхронных и многопоточных приложений (asyncio, aiohttp, multiprocessing и др.) — опыт взаимодействия с реляционными и нереляционными базами данных — практический опыт настройки и поддержки CI/CD-пайплайнов (Jenkins, GitLab CI, ArgoCD или аналоги) — опыт развёртывания и управления приложениями в Kubernetes. Условия: — комфортный современный офис рядом с м. Кутузовская — гибридный формат работы — ежегодный пересмотр зарплаты, квартальная и 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.

Location: London Workplace: Hybrid ABOUT NIANTIC SPATIAL We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. ABOUT THE ROLE We are looking for 3D Reconstruction Research Scientist to join our Research Team in London. Our team researches and builds the 3D reconstruction technology behind Niantic Spatial's platform. These pipelines turn casually captured or domain specific imagery into high-fidelity explorable 3D reconstructions. This entails anything from Structure-from-Motion through to 3D Gaussian Splatting, feed-forward models, and generative models. Our team shipped 360 splatting to consumers and enterprise customers, developed the splatting tech that runs on phones, shipped multiple POCs to enterprise customers, and have innovated on countless AR technologies. Our team has published at top venues (CVPR, ICCV, ECCV, SIGGRAPH); more often than not, our papers end up in production. We're hiring a researcher to work at both ends of that pipeline. You will implement and iterate on the state-of-the-art for both academic publishing and production, regularly working on prototypes with a short turnaround into customer-facing production. You will work alongside world-class researchers in computer vision, machine learning, graphics, and robotics to develop new approaches for large-scale spatial understanding, localization, reconstruction, scene understanding, and geospatial foundation models. WHAT YOU’LL DO - Conduct original research in computer vision, machine learning, and spatial AI. - Design, implement, and evaluate novel algorithms for: - 3D reconstruction - Neural scene representations - 3D Gaussian Splatting - Geometric deep learning - Structure-from-Motion (SfM) - Meshing - Feed-forward models - Generative models - Spatial foundation models - Collaborate with research scientists and engineers to transition promising ideas into future products. - Develop large-scale experiments and benchmarking frameworks. - Work directly with the realities of production with real capture conditions, real hardware constraints, and real customers. - Publish research findings at leading conferences such as CVPR, ICCV, ECCV, or SIGGRAPH. WHAT YOU’LL BRING - PhD in Computer Vision, Robotics, Machine Learning, Computer Science, Mathematics, Physics, or a closely related field. Exceptionally strong candidates with equivalent experience in industry would be considered. - A strong research track record in 3D computer vision: reconstruction, depth estimation, novel view synthesis, SfM, SLAM, or closely related areas. - Strong publication record at top-tier conferences such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, etc. - Strong programming skills in Python and experience in PyTorch. - Evidence that you can carry an idea from research into something real; that may be a shipped feature, a production pipeline, a widely used open-source release, or similar. - Ability to independently drive research projects from idea generation through experimentation and publication. NICE TO HAVE - Experience with classical reconstruction tooling (COLMAP or similar). - C++ or CUDA experience, and a feel for performance and on-device constraints. - Experience with traditional 3D techniques — meshing, texturing, or bringing modern geometry or generative representations into native 3D. COMPENSATION & BENEFITS Base salary: Competitive market rate. Compensation also includes an annual bonus, equity, and a comprehensive benefits package. LOCATION & WORK MODEL This role is based in our London office, with three days per week in office. INCLUSIVE APPLICATION We know the strongest candidates don’t always tick every box. If you’re excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn’t match every qualification listed — you may be exactly who we’re looking for. EQUAL OPPORTUNITY Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. CANDIDATE PRIVACY I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial’s Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: San Francisco, CA, USA About the role Chime's Data Science & Machine Learning team builds the models, services, and platforms behind how millions of members manage and grow their financial lives. We're hiring a Staff AI/ML Engineer on our Lending and Cashflow team to lead the modeling behind cash flow underwriting and surface member insight: using members' transaction and deposit data to make fair, accurate credit decisions for people that traditional credit scores often miss. In this role, you'll build and deploy machine learning systems on some of the richest transactional and behavioral data in fintech, turning it into the credit decisions behind products like MyPay, Instant Loans, and SpotMe. This is a highly applied role: you'll own problems end to end, from framing the question through to a model running in production and moving a metric that matters, and you'll set the technical direction for how Chime underwrites with cash flow data. In this role, you can expect to Lead the design, training, and deployment of deep learning and classical ML underwriting models that turn members' transaction and cash flow data into credit decisions, from approval and line assignment to loss forecasting Take models from problem framing through to production — training, evaluation, deployment, monitoring, and iteration — and stay accountable for how they behave once they're live Design and improve the systems around the model: feature pipelines, batch and real-time inference, monitoring, and retraining Partner with Product, Engineering, Analytics, and Risk to turn ambiguous business problems into ML solutions, and to make sure the solution is the right one Connect model performance to member outcomes and business metrics, and use experimentation to prove impact Set the technical roadmap for Lending ML and raise the bar across the team through design reviews, mentorship, and shared standards for model development, validation, and governance Help identify where AI/ML creates measurable impact for members — and where a simpler answer is the better one To thrive in this role, you have A track record of leading the design and deployment of deep learning models in production, including multi-quarter initiatives that span teams, with a solid grasp of architecture choice, training dynamics, and evaluation, and the judgment to make model design choices Solid machine learning fundamentals: classical modeling, evaluation design, and knowing which metric actually answers the question in front of you Hands-on experience across the end-to-end ML lifecycle — training, experimentation, optimization, deployment, and monitoring Comfort with messy real-world data, including label definition, leakage, class imbalance, and train/serve skew Strong proficiency in Python and SQL, with deep learning frameworks such as PyTorch and distributed compute such as Spark or PySpark Working knowledge of modern ML infrastructure — AWS and tools such as SageMaker, Airflow, Kafka, Redis, and Snowflake — and an MLOps mindset for keeping production systems healthy The ability to operate independently in ambiguous environments, influence technical direction beyond your own team, and explain model decisions clearly to engineers, risk partners, and senior leaders Nice-to-have Experience in either of these is a plus: Credit or underwriting models, and familiarity with model governance, explainability, and fairness requirements, such as adverse action reason codes and fair lending testing Cash flow or alternative-data underwriting: building credit features from bank transaction data, such as income and expense detection, balance trends, and transaction categorization Deployed transformer models in production #LI-Onsite #LI-DA1 Below is the base salary offered for this role and level of experience. Full-time employees may also be eligible for bonus(es), competitive equity, and benefits. For commissioned roles, the base salary listed in this job description does not include incentive/variable pay. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. Wage Notice $246,000 — $340,000 USD A little about us At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential. We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members. We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deeply entrepreneurial, encouraging every Chimer to see themselves as stewards of our mission to help everyday Americans unlock their financial progress. We know that to achieve our mission, we must earn and keep people's trust—so we hold ourselves to the highest standards of integrity in everything we do. These aren't just words on a wall—our values are embedded in every aspect of our business, serving as a north star that guides us as we work to help millions achieve their financial potential. Because if we don't—who will? *Chime is a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC. What we offer for our full-time, regular employees 🏢 Our in-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location. 💻 Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees. ✨ Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life. 🏝 Generous vacation policy and company-wide paid days off 🫂 1% of your time off to support local community organizations of your choice 👟 Annual wellness stipend to use towards eligible wellness related expenses 👶 Up to 22 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents 👪 Access to family planning reimbursement 💚 A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress We know that great work can’t be done without a diverse team and inclusive environment. That’s why we specifically look for individuals of varying strengths, skills, backgrounds, and ideas to join our team. We believe this gives us a competitive advantage to better serve our members and helps us all grow as Chimers and individuals. Chime is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the San Francisco Fair Chance Ordinance, Cook County Ordinance, NYC Fair Chance Act, and the LA City Fair Chance Ordinance, and consistent with Canadian provincial and federal laws. If you have a disability or special need that requires accommodation during any stage of the application process, please contact: accommodations@chime.com . To learn more about how Chime collects and uses your personal information during the application process, please see the Chime Applicant Privacy Notice .

Location: 🇧🇷 São Paulo / 🌎Remote Workplace: Remote A little bit about us: We are Belvo, an open finance API platform with the bold vision of democratizing access to financial services in Latin America. We enable any financial innovator to access and interpret financial data, as well as initiate payments from their end-users accounts. We’re turning the messy complexities of the Latin American financial ecosystem into a modern set of tools to access and interpret data and move money in a seamless and secure way. We’re a highly-technical, passionate, and driven team. We are more than 90 people and our team currently represents 20 nationalities. We have offices in São Paulo and Mexico City – while a large portion of us work remotely. We are tackling a very stimulating problem: connecting fintech innovators with legacy financial infrastructure. We strive to go beyond the limits of what is possible today and to do so in an elegant and developer-first way. Since starting our adventure in May 2019, we have raised $71m from the leading VC investors globally. You can read more about our company here https://belvo.com/about/ and about our team and culture here https://belvo.com/careers/. Also, head to our blog https://belvo.com/blog/ for more news about what we’re building and how we work https://belvo.com/blog/category/working-belvo/. About the team: - We work in cross-functional, autonomous teams. We follow continuous delivery best practices executed on top of a modern technology stack.  - Our products are built for developers, by developers. Technological excellence is at the heart of what we do.   - We are pragmatic and customer-focused. We strive to find the right set of trade-offs in order to validate our hypothesis as early as possible, iterating on our products based on customer feedback.  - We communicate transparently. We do weekly all-hands where we get together to discuss company performance and goals. - While we are global and remote-friendly, we also operate from our vibrant offices in CDMX and Sao Paulo. To accommodate the various time zones in which we are based, we ensure we’re always synced up for at least 3 hours per day. - Also, we are backed by some of the leading investors in Silicon Valley and Latin America, including Founders Fund, Kaszek Ventures, and YCombinator. The challenge The Intelligence Squad is Belvo's AI enabler team. One part of the squad builds the platforms and patterns that let every team at Belvo ship AI. This role sits in the other part: turning the financial data and payments we aggregate across Latin America into models that make real decisions, who looks creditworthy, which transaction is fraudulent, what category a payment belongs to, whether an income statement checks out. That data drives real financial outcomes for the people and businesses on the other end, so the bar for rigor is high. You'll be the data science reference in the squad. We're not hiring someone to execute a modeling backlog someone else wrote. We want someone who decides which problems are worth modeling, defines how we know a model is good enough, and raises the data science bar for everyone they work with. You'll be deep enough in statistics and ML to defend a modeling choice to anyone, whether this is a set of rules to simplify a problem or a complex model that you build. You'll be close enough to the business to turn a commercial problem into a modeling roadmap and a business case. And you'll be a strong enough engineer to take your models from notebook to production and own them there. Responsibilities - Model development and selection: Lead the design of statistical and ML models (classification, regression, ranking, NLP or LLM-based) for problems like risk scoring, fraud detection and transaction categorization. Choose approaches on their merits and defend those choices with data, not habit. - AI-leveraged delivery: Use agentic coding and research tooling as a core part of how you work: exploring data, generating and testing feature hypotheses, scaffolding pipelines, writing the service around the model. We care about the decisions you ship, not hours at the keyboard, and we expect you to keep compressing the gap between the two. - Feature engineering with business context: Work with Product and the business teams to figure out which signals in Belvo's financial data actually explain the outcome you're modeling, and translate an ambiguous business question into a well-scoped modeling problem. - Productionization and ownership: Take models past the notebook. Build or shape the pipelines, APIs and services that run them reliably in production, and own them once they're live: monitoring, drift, retraining, and retirement when the evidence says so. - Experimentation: Design and run experiments (A/B tests and offline evaluation) to confirm that a model change actually moves the metric it's meant to move. - Cross-functional partnership: Work closely with Engineering, Data Research, Product, and the rest of the Intelligence Squad to ship end to end, and with stakeholders across the business to keep your models grounded in what actually matters to them. - Business and customer partnership: Work directly with stakeholders and, when needed, customers. Translate ambiguous business questions into well-scoped problems, and explain trade-offs and impact clearly to non-technical audiences, executives included. - Staying current: GenAI moves weekly. We expect you to be genuinely plugged in: model releases, papers, and tooling, to hold an opinion on what's real versus hype, and to bring the useful parts back to the squad as a demo, a doc, or a session for our company-wide AI Chapter. - Framing the problem: Decide what's worth modeling. Push back when a rule, a heuristic, or a better data pipeline solves the problem without a model, and be willing to kill or retire your own model when the evidence says so. This position may be for you if - 5+ years of experience as a Data Scientist, Applied Scientist, or in a similar hands-on modeling role. - You've owned models end to end in production, from framing the problem to monitoring it live, and you can point to the measurable business impact they had. - You have a solid grounding in statistics and can explain how your models work at that level, not just how to call them. - You've built and compared more than one type of model (for example tree-based methods, regression, deep learning) for the same problem and can justify why you picked the one you shipped. - You're comfortable in Python for data science (pandas, scikit-learn, and at least one of TensorFlow or PyTorch) and in SQL for pulling and shaping your own data. - You've taken several models you built into production yourself, not just handed it off to someone else to deploy.You http://deploy.You understand engineering fundamentals: testing, version control, CI/CD, and how to write code someone other than you can maintain. - You've built with LLMs past the API-call level: tool use or agents, retrieval, structured outputs, and shipped at least one to production. You can explain how you knew it was good enough, which means you've built some form of evaluation, not just eyeballed outputs. - Agentic coding tools are part of your daily workflow, not a novelty. You can walk us through a specific task that used to take you days and now takes hours: how you set it up, what you delegated, and where you still don't trust the output. - You're proficient in written and spoken English. Amazing if - You've worked in fintech, financial risk, or another regulated data environment before. - You have a business or strategy background alongside data science (an MBA, consulting, or a product/strategy role). - You've worked in a customer-facing data science role, such as a forward-deployed engineer or a solutions/applied team at a data platform company. - You've worked with MLOps tooling for model versioning, monitoring, and automated retraining. - You've deployed models on AWS (for example SageMaker or an equivalent managed ML service). - You've written evals or an LLM-as-judge harness for a non-deterministic system, and have opinions about where that approach breaks. - You've used LLMs inside the data pipeline itself: labeling, extraction, categorization, and can say where they beat a classical model and where they quietly don't. Tech stack - Python is our primary language for data science and modeling (pandas, scikit-learn, and deep learning frameworks as needed). - SQL against a cloud data warehouse for data access and feature work.Claude deployed company-wide and Claude Code for every engineer. Agentic tooling is part of how we work daily, not a side experiment. - Cloud infrastructure on AWS, favoring managed services so you can focus on the modeling problem rather than infrastructure plumbing. - An experimentation platform for A/B testing and offline evaluation. - We use Datadog for service observability across the company. - We follow Continuous Integration and Continuous Delivery best practices. Our tech stack: - We’re building our platform using modern technologies, putting the focus on reliability and long-term maintainability - We primarily use Python on the backend. We use battle-tested technologies such as Django, and we are heavy users of Python’s asyncio for some parts of our stack - We use Javascript, Vue.js and Sass on the frontend. We are developing and leveraging our own design system and component library - We run our infrastructure on top of Amazon Web Services, leveraging managed services. We tend to favor the use of managed services wherever possible, in order to focus on our business problems - We observe and monitor our services using Datadog - We follow Continuous Integration and Continuous Delivery best practices Our process steps: At Belvo every hire is so important to us, that we share the decision to hire as a team. - People team chat - Take-home challenge - Challenge presentation - Team fit chat - Meet the founders Our perks: 🚀 Stock options (we are all owners and this is very important to us) 📈 Annual company bonus linked to company performance 🔆 Flexible working hours 🔝 Remote friendly 🐶 Pet friendly 👩🏻‍⚕️ Health Insurance 🎊 Paid time off on your birthday 💻 Renew your laptop every 3 years 🙋 Training Budget 😎 Team building events 🙀 Bank holidays swap within 30 days 🔋 Fitness/ wellness stipends 🚀 Yearly company offsite 🍒 Fresh fruit every week, all-you-can-drink tea and coffee 🏝 Extra days off accrued when completing 2, 3, 4 years and beyond at Belvo 🎳 Yearly department offsite

• Взаимодействие с data scientists, инженерами, аналитиками и бизнес-стейкхолдерами. • Коммерческий опыт в ML/ML Engineering от 3 лет, уровень Middle+ / Senior.

Location: Москва Мы — технологическая команда, создающая инновационные решения для спорта. Наш продукт направлен на трансформацию и объективизацию судейства. Мы ищем опытного специалиста в области MLE для участия в разработке и развитии наших решений. **Предстоит** работать с ключевым функциональным и архитектурным ядром нашей системы видеоаналитики, а именно: Обязанности * работать со скелетом как с многомерным временным рядом: сегментация выступления на элементы и фазы, детекция границ (отрыв, приземление, точки экстремума), выделение повторяющихся циклов, temporal action detection на видеопотоке, сглаживание и агрегация предсказаний по времени * участвовать в проектировании API, внутренних контрактов и структуры данных * оптимизировать модели для достижения целевых показателей задержки * развивать высокопроизводительный Python-фреймворк для CV/ML-пайплайна на более чем 15 моделей, работающих единовременно * реализовывать механизмы обработки с multiprocessing и multithreading. Обеспечивать эффективную оркестрацию между CPU и GPU-ресурсами * минимизировать накладные расходы на межпроцессное взаимодействие, копирование данных и работу с памятью. Проводить профилирование и устранять узкие места в video I/O, decode, inference и postprocessing * проводить профилирование и устранять узкие места на всех этапах: Video I/O, декодирование, препроцессинг, инференс, постобработка. Требования * опыт работы ML Engineer / CV Engineer от 3 лет * высокое качество кода на Python (OOP, Pydantic, SQLAlchemy, для DS - NumPy, OpenCV, PyTorch) * опыт реализации сложной вычислительной или бизнес-логики * практический опыт разработки production-ready ML/CV pipeline * опыт работы с видеоаналитикой, обработкой видео или потоковых данных * понимание особенностей Python runtime для высокопроизводительных пайплайнов: GIL, IPC, serialization, shared memory * опыт использования инструментов ускорения инференса: ONNX, TensorRT, TorchScript или аналогов * опыт работы с Linux, Docker, работы в команде в Git, CI/CD Будет плюсом: * опыт разработки расчетных систем * PostgreSQL, ClickHouse, SQLAlchemy * владение компилируемыми языками, такими как Go или C++, для реализации или оптимизации критичных по производительности частей системы * опыт работы со спортивной аналитикой или системами судейства. Условия * комфортный современный офис рядом с м. Кутузовская  * гибридный формат работы * ежегодный пересмотр зарплаты, квартальная и 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.

Location: Austria; France; Germany; Italy; Netherlands; Poland; Spain; United Kingdom Why Kimchi? Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today we run our own coding harness on any model you pick, and the router deciding which model answers each request is ours too. The Auto Router Our promise is one enforcement layer for your AI: budgets, spend attribution, and model routing, applied before a request ever hits a model. The Auto Router makes the call: for every request, which model serves it. Eight open-source models whose input prices span more than 20×, cached input up to 50× cheaper than fresh, frontier providers on customers' own keys, and a coding agent that fires hundreds of requests before its task is done. Frontier-level task success at a fraction of the token cost: that sentence is the product, and it stays true only if the model inside the router keeps getting better. That model is yours. Made by Cast AI — a Miami-headquartered, globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. About the role Every request gets a decision: which model serves it. This is an applied-ML seat, not an infrastructure one. Platform engineers own the proxy, the streaming path, and the fleet. You own the model inside them: the training data, the objective, the evaluation, and the iteration loop that ships a better router every cycle. The problem: routing is a prediction problem wearing a product's clothes. For every request you predict the quality each candidate model would deliver, what it would cost, and how fast it would decode, and you have to be right enough, in aggregate, that the savings are real and the quality never dips below frontier. The data is messy: rollout outcomes from benchmark harnesses, per-request records from the live fleet, override signals, and token streams whose speed depends on the serving concurrency at the moment. Speed itself is a correctness variable: on deadline-bound agentic work, a model that decodes three times slower doesn't do the same work slower, it does less work. That's why naive cost-per-token objectives quietly optimize for the wrong thing. And the bar for evidence is high: a router change ships only after production-parity evaluation with error bars, then gets held to its numbers in live traffic. Stack: Python; PyTorch; SQL at scale (ClickHouse); OpenAI- and Anthropic-compatible LLM APIs; benchmark and rollout harnesses (RouterBench-class); GitLab CI. Requirements You've spent 5+ years in applied ML or ML research, and you've trained, evaluated, and shipped models that make real-time decisions (ranking, recommendation, bandits, pricing, or LLM routing) and you can tell that story with receipts. Strong Python and SQL at scale: you can pull, clean, and interrogate millions of request-level records without waiting on anyone. You know the difference between a model that got better and an eval that got easier. You have real opinions about what LLM benchmarks measure, agentic harnesses included. You know enough serving reality (TTFT, decode speed, batching, prefix caching, concurrency) to model the objective honestly. You instrument before you optimize, error bars and all. A wide mandate excites you more than it unsettles you. Nice to haves You've worked on LLM routing specifically: RouterBench-class evals, cost/quality/latency Pareto fronts. Calibration, abstention, or conformal methods in production decision systems. You've run online A/B loops with drift monitoring on a model that touches real money. Responsibilities Own the routing model end to end: features, objective, training, calibration, and release cadence. Iterate it like a product, not a research artifact. Build the evaluation that gates every change: production-parity benchmarks, router-vs-router comparisons, holdouts by task family, error bars on pass rates. Nothing ships on vibes. Turn fleet data into training signal: per-request outcomes from production, benchmark rollouts, and override feedback, with failure categorization (infra vs task) so poisoned labels never reach the policy. Model the objective the user actually experiences: task success × token cost × wall-clock speed under the real concurrency regime, with cached-input economics in the picture. Run the online loop: shadow, A/B, full rollout, drift monitoring, retrain. When production disagrees with your offline eval, you find out why before the next ship. Push the frontier where it pays: calibrated decisions, abstention under uncertainty, speed-aware routing. We experiment on what a router should be, not just which model it picks today. Partner with platform engineers on instrumentation: the features and labels recorded per request (decode speed first-class among them) and the experiments the data path must support. What’s in it for you? Competitive salary (depending on the level of experience). Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Austria; France; Germany; Italy; Netherlands; Poland; Spain; United Kingdom Why Kimchi? Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today we run our own coding harness on any model you pick, and we run the models underneath it ourselves. Kimchi Inference One API line and a customer's existing client just works: DeepSeek, GLM, Kimi, MiniMax, Nemotron served serverless from our own GPU fleet (OpenAI- and Anthropic-compatible, pay per token, spend visible per user, team, and key). When control matters, the same models run on the customer's own cluster instead. Behind that one line sits the machinery you'll own: SGLang and vLLM engines, B300 nodes, a shared cluster filesystem that holds every checkpoint exactly once, KV caches riding coding traffic with heavy prefix reuse, and the benchmark harness that gates every change we ship. Made by Cast AI — a Miami-headquartered, globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. About the role Throughput. Latency. Cold starts. Cost per token. Move those four numbers in the right direction and two things happen: customers get faster, cheaper inference, and our margins improve. That's the entire thesis of this role. Every loader you tune, every scheduler decision you land, every kernel you stare at shows up in a customer's p99 and on our P&L. Your mandate is the inference stack's optimization, whatever it takes, and the technical direction of how we optimize it is yours to argue with data. The problem: the checkpoints run 0.3-1.5 TB, and the "right" serving configuration depends on traffic shape, sequence-length distribution, batch dynamics, GPU SKU, memory bandwidth, and quantization tolerance, all variables that shift week to week. Most teams pick a configuration once, over-provision GPUs, and absorb the cost. We don't: we took Kimi-K3 (1.5 TB) from over an hour cold-to-serving to under twelve minutes with a shared filesystem, O_DIRECT weight loaders, and node-local JIT caches, and we're nowhere near done. Prefill/decode disaggregation and tiered KV caches are next. Stack: Python; SGLang; vLLM; TensorRT-LLM; PyTorch; fastsafetensors; CUDA-adjacent tooling; Kubernetes; virtiofs shared filesystems; ClickHouse; OpenTelemetry; Prometheus; Grafana; GitLab CI. Requirements You've built real ML systems for 5+ years, and your portfolio shows depth in inference or training infrastructure, not just model training notebooks. Your Python is production-grade: services, not scripts. You've run vLLM, SGLang, or TensorRT-LLM for real, and you have a working mental model of why an engine performs the way it does on a given GPU. You've measured quantization regressions, not just compression ratios. Multi-GPU and multi-node setups hold no fear: tensor and expert parallelism, collective communication, and their practical failure modes. You know where the bytes live (page caches, O_DIRECT, network filesystems under terabyte-scale reads) and how fast they move. You instrument before you optimize, and you can tell a real win from a benchmark artifact. A wide mandate excites you more than it unsettles you. Nice to haves You've worked on LLM routing specifically: RouterBench-class evals, cost/quality/latency Pareto fronts. Calibration, abstention, or conformal methods in production decision systems. You've run online A/B loops with drift monitoring on a model that touches real money. Responsibilities Push throughput. Continuous batching, chunked prefill, speculative decoding with draft models, CUDA-graph capture, and scheduler tuning across the engine fleet. Find the ceiling on each GPU SKU, then raise it. Cut latency. Attack TTFT and inter-token latency separately; profile to find the actual bottleneck (compute, memory bandwidth, scheduling, storage, or network) and fix it, not the one you assumed. Get more out of the KV cache. Hierarchical and paged attention, prefix caching on heavy-reuse coding traffic, eviction policies, quantized KV. A lot of the unrealized throughput lives here, and you'll own it. Kill cold starts. Weight loaders (O_DIRECT, fastsafetensors-class), JIT and compile caches, shared-filesystem read paths. A 1.5 TB checkpoint has no business taking an hour to reach serving. Quantize without regressing quality. INT8/INT4, FP8/FP4 across weights, activations, and KV, measured on real workloads, not just perplexity benchmarks. Scale across nodes. Distributed inference topologies, network-aware placement, prefill/decode disaggregation, checkpointing strategies that don't bottleneck on storage or interconnect. Set the technical direction. Decide what we benchmark, what we adopt, and what we build ourselves. Bring the team along with strong writeups and reproducible experiments. What’s in it for you? Competitive salary (depending on the level of experience). Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: 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 are looking for a strong Deep Learning Engineer to join our Autonomy team and develop the learning-based algorithms that power dynamic locomotion and whole-body control on our humanoid robots. You will develop robust, reusable motion capabilities that enable the robot to move dynamically, maintain balance, interact with its environment, and combine locomotion with manipulation. This includes building the underlying learning-based motion primitives that can be composed into increasingly complex whole-body behaviors. You will work closely with robotics, controls, simulation, and machine learning engineers to turn learned behaviors into reliable capabilities running as part of the robot's real-time control stack.   WHAT YOU'LL DO - Design and train learning-based locomotion and whole-body control policies for dynamic robotic behaviors. - Develop motion primitives using different learning based generative models. - Apply reinforcement learning, imitation learning, and other learning-based approaches to motion generation and control. - Build scalable simulation, data generation, and training pipelines using platforms such as Isaac Lab and MuJoCo. - Develop methods for robustness, adaptation, and sim-to-real transfer, including domain randomization and learning from real-world robot data. - Deploy, evaluate, debug, and fine-tune learned policies on physical humanoid robots. - Optimize policy inference and training systems for the performance and latency requirements of real-time control. - Integrate learned policies into the broader robot control and autonomy stack.   WHAT WE'RE LOOKING FOR - BS, MS, or PhD in Robotics, Machine Learning, Computer Science, or a related technical field. - 3+ years of experience developing machine learning systems in industry or research. - Strong understanding of deep learning and modern policy architectures, including training, inference, experimentation, and debugging. - Hands-on experience with RL and at least one additional area such as imitation learning, generative models, sequence models, or large-scale representation learning. - Strong Python and PyTorch experience. - Experience with physics-based simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or similar platforms. - Ability to reason about learning algorithms in the context of dynamics, control, and physical systems. Nice to have: - Experience applying reinforcement learning or imitation learning to legged robots, humanoids, or other complex physical systems. - Experience with whole-body control, locomotion, motion imitation, or loco-manipulation. - Experience deploying learned policies on real robotic systems and debugging the sim-to-real gap. - Familiarity with motion capture, trajectory optimization, model-based control, or learning from demonstrations. - Experience training policies at scale across large numbers of simulated environments. - Publications at NeurIPS, ICML, ICLR, CoRL, RSS, CVPR, or equivalent contributions to impactful open-source robotics or machine learning projects.   WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Задачи: — разработка ПО на Python с использованием FastAPI, PostgreSQL — организация интеграционного, unit и регрессионного тестирования — настройка и обеспечение бесперебойной работы, разработки, сборки и дистрибуции программного обеспечения (Gitlab, Docker, etc.) — проработка и внедрение технологий развертывания и обеспечения бесперебойной работы прикладного ПО — развертывание тестовых и основных стендов — взаимодействие с командой по разработке электроники и конструкторами для интеграции программного обеспечения с аппаратной частью. Требования: — высшее техническое образование в области робототехники или смежное направление — отличное знание Python — знание ООП и паттернов программирования — знание сетевых технологий и протоколов (tcp/udp, http, rest api, web socket) — уверенные знания ОС семейства Linux — опыт организации сборки и поставки проектов (Python, Java и С++) для Linux — опыт работы с инструментами CI/CD (Gitlab, Jenkins) и системами управления конфигурациями (ansible, etc.) Будет преимуществом: — опыт разворачивания мониторинга с использованием Zabbix/Prometheus — знание OpenCV и опыт работы с PyTorch и CUDA — опыт работы с Python для разработки робототехнических приложений — общее понимание современных ИИ-инструментов и их возможностей. Условия: — комфортный современный офис г. — Москва, МЦК ЗИЛ, Автозаводская 23а к2 — формат работы - лаборатория робототехники — 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 (для вакансий уровня Junior) — 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: — Разработка AI агентов на основе современных генеративных сеток (GigaChat и OpenSource); — Разработка системы офлайн- и онлайн-метрик для оценки качества LLM-приложений; — Следить за последними исследованиями и инновациями в области языковых моделей и применять их на практике. Требования: — Опыт разработки AI агентов для компаний end-to-end от исследования до запуска в продакшн; — Глубокое понимание архитектуры нейронных сетей, трансформеров и их применения в моделях LLM; — Экспертное владение Python-библиотеками для AI/ML (PyTorch, Transformers, LangChain, LlamaIndex, LangGraph, pandas, NumPy, scikit-learn); — Знание MLOps-инструментов (Docker, LangFuse, Ollama, vLLM); — Высокий уровень знаний статистики (теорвер + матстат), глубокое понимание ключевых алгоритмов ML. Условия: — Работа в аккредитованной IT-компании; — Удобный офис (м. Калужская, ул. Обручева 30/1 с.2); — Ежегодный бонус по итогам работы; — Подключение к ДМС, телемедицина, чек-ап после испытательного срока; — Ведение беременности и родов, лазерная коррекция зрения и стоматология после двух лет непрерывного стажа в группе Сбер; — Специальные условия для страхования семьи; — Возможность участия в корпоративной пенсионной программе; — Корпоративное обучение; — Бесплатная подписка на СберПрайм и скидки у партнёров; — Новогодние подарки для детей, корпоративные мероприятия и поддержка в особых жизненных ситуациях; — Скидки в спортклубы X-Fit, WorldClass. Дополнительно: — Видимый результат: вклад напрямую влияет на продукт и развитие компании; — Минимизация бюрократии, возможность проявлять инициативу; — Возможность карьерного роста внутри команды или других подразделений; — Вклад в развитие общества через исследования, способствующие улучшению экономики регионов и качества жизни людей; — Вера в силу данных как инструмента позитивных изменений.

Задачи: — проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы (индексация, ретривал, ранжирование, генерация) — разрабатывать автономных AI-агентов с использованием планирования, вызова инструментов (function calling) и мультиагентной оркестрации — обучать и дообучать NLP-модели (classification, embeddings) и современные LLM под бизнес-задачи (SFT, LoRA/QLoRA) — строить системы оценки качества генерации, внедряя продуктовые и ML-метрики (RAGAS, DeepEval, кастомные LLM as a judge) — переводить бизнес-задачи в ML-постановку и реализовывать решения полного цикла от гипотезы до интеграции в продакшен. Требования: — опыт работы в Deep Learning от 3 лет, включая обучение моделей на PyTorch — продвинутое понимание архитектуры современных LLM, включая механизмы attention, MoE, принципы инференса (KV-cache, batching) и методы alignment — практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain, LangGraph или аналогичных фреймворков — уверенная база в классическом ML и NLP, знание статистики, алгоритмов поиска и векторизации (от TF-IDF до эмбеддингов) — опыт работы с фреймворками для оценки и мониторинга LLM-решений, такими как Arize, LangSmith или Weights & Biases — владение техниками промпт-инжиниринга и опыт их применения на практике для оптимизации работы моделей — уверенное владение Git и Linux-окружением. Будет плюсом: — базовое понимание принципов проектирования архитектуры микросервисов — опыт опромышливания AI-агентов и знание принципов LLMOps, включая CI/CD, оркестрацию и управление версиями промптов — глубокое понимание архитектуры масштабируемых RAG-систем и опыт работы с векторными базами данных — опыт разработки асинхронных сервисов (asyncio) и интеграции ML-моделей в высоконагруженные системы. Условия: — комфортный современный офис рядом с м. Кутузовская — гибридный формат работы — ежегодный пересмотр зарплаты, квартальная и 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.

Location: Canada About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Customer Experience: The Customer Experience Team at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community. The role We are seeking a highly skilled and customer-focused professional to join our team as a Solutions Architect specializing in Cloud infrastructure and MLOps . As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines. You’re welcome to work remotely from Canada. Your responsibilities will include: Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices. Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers. Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI. Act as a single point of expertise of customer scenarios for product, technical support, marketing teams. Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.) We expect you to have: Bachelor's degree or foreign equivalent in a related field, or an equivalent combination of education and relevant experience 5+ years of customer-facing technical pre-sales experience, with pre-sales as a primary responsibility, and demonstrate ownership of technical discovery, solution architecture, customer presentations, and proof-of-concept delivery for cloud infrastructure or AI platforms, translating customer requirements into solutions and guiding technical buying decisions. 5+ years of experience utilizing Infrastructure as Code (IaC) to automate the deployment, configuration, and management of scalable cloud infrastructure and network architectures. 5+ years of Kubernetes experience designing, provisioning, and operating clusters, including node management, networking (CNI), storage (CSI), scheduling, scaling, and troubleshooting beyond managed-service abstractions. 5 years of experience developing Python-based solutions for cloud infrastructure automation, systems integration, and technical problem-solving. It will be an added bonus if you have: 3+ years of experience with GPU computing for machine learning and artificial intelligence workloads as training and inference. Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow) Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch) Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google) Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. On Target Earnings Range $235k - $300k CAD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Remote - United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Customer experience: Customer experience at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions. Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community. The role We are seeking a highly skilled and customer-focused professional to join our team as a Solutions Architect specializing in Cloud infrastructure and MLOps . As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines. You’re welcome to work remotely from the United States. Your responsibilities will include: Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices. Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers. Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI. Act as a single point of expertise of customer scenarios for product, technical support, marketing teams. Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.) We expect you to have: Bachelor's degree or foreign equivalent in a related field, or an equivalent combination of education and relevant experience 5+ years of customer-facing technical pre-sales experience, with pre-sales as a primary responsibility, and demonstrate ownership of technical discovery, solution architecture, customer presentations, and proof-of-concept delivery for cloud infrastructure or AI platforms, translating customer requirements into solutions and guiding technical buying decisions. 5+ years of experience utilizing Infrastructure as Code (IaC) to automate the deployment, configuration, and management of scalable cloud infrastructure and network architectures. 5+ years of Kubernetes experience designing, provisioning, and operating clusters, including node management, networking (CNI), storage (CSI), scheduling, scaling, and troubleshooting beyond managed-service abstractions. 5 years of experience developing Python-based solutions for cloud infrastructure automation, systems integration, and technical problem-solving. It will be an added bonus if you have: 3+ years of experience with GPU computing for machine learning and artificial intelligence workloads as training and inference. Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow) Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch) Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google) Key Employee Benefits: #LI-SM2 Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) Plan: Up to 4% company match with immediate vesting. Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote Work Reimbursement: Up to $85/month for mobile and internet. Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. On Target Earnings Range $250,000 — $320,000 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius is looking for a Principal Product Marketing Manager to set the direction for our enterprise story: the narrative, messaging, and go-to-market strategy that turn Nebius’s full-stack differentiation into pipeline and revenue with mid-market and enterprise buyers. You’re welcome to work remotely in the United States. This is a role for a recognized enterprise product marketing leader from B2B cloud or AI infrastructure who has shaped category narratives and led complex go-to-market motions, and can prove it through the messaging they built, the programs they orchestrated, and the revenue outcomes behind them. You understand the AI cloud stack well enough to earn credibility with engineering leaders, and you know what it takes to move a complex enterprise buying committee (CIOs, CTOs, Chief Data/AI Officers, CISOs, VPs of Infrastructure, security and procurement teams) through a 6–18-month cycle. You set the strategy and frameworks that others execute against, and you measure your impact in win rates, pipeline influenced, and field adoption, not assets shipped. Reporting to the Senior Director, Product Marketing, you will be the senior voice for how Nebius positions itself to the enterprise and a force multiplier for the organization: setting the enterprise go-to-market strategy across product lines, guiding and mentoring other product marketers, and equipping sales, solutions architecture, and partnerships to carry the story into the field. You will work directly with senior leaders across product, engineering, sales, and executive leadership as a trusted strategic partner. Your responsibilities will include: Set the enterprise narrative and positioning strategy. Own the enterprise layer of the Nebius story end to end, from our “ultimate AI cloud” positioning down to persona-specific messaging, and define the frameworks and standards that other product marketers and content teams build on. Translate our differentiators and each layer of the cloud stack (custom GPU servers, AI orchestration and MLOps tooling, Token Factory, and our agentic layer) into credible messaging that resonates with technical and business decision-makers alike. Lead enterprise go-to-market strategy across products and launches. Define the enterprise go-to-market plan across the Nebius portfolio, set priorities and sequencing for major launches, and drive alignment across product, sales, solutions architecture, partnerships, and marketing. Make the trade-offs on where to focus and invest, and hold cross-functional teams accountable to shared outcomes. Design and own the enterprise customer journey. Own the logic and strategy of the 6–18-month enterprise buying cycle, defining what moves a buyer at each stage from awareness to evaluation, decision, and expansion. Set the plays, content, and touchpoints for each stage and partner with digital/ABX and field marketing leaders to sequence and target them by account, using ABM platforms such as 6sense or Demandbase. Build the enterprise business case. Develop the messaging, proof, and business case around what enterprises actually evaluate: total cost of ownership, reliability and SLAs, security and compliance, data sovereignty and residency, migration risk, and vendor lock-in. Equip Nebius to win through security review, procurement, and the CFO’s office, and engage directly with senior executives on strategic deals. Drive enablement strategy and field adoption. Set the enablement strategy for sales, solutions, and partner teams, and ensure they can articulate Nebius’s value to every persona on the buying committee: first-call decks, battlecards, discovery guides, ROI/TCO tools, objection handling, and competitive positioning against hyperscalers and neoclouds. Lead enablement programs and measure adoption, win rates, and deals influenced. Own competitive and category positioning. Maintain a sharp, evidence-based view of the AI enterprise infrastructure landscape and help shape how the category is defined. Turn market and competitive intelligence into positioning the field can defend and campaigns can lead with, and keep Nebius’s differentiation current as the market moves. Lead the enterprise content and proof strategy. Define the enterprise asset strategy and own quality across the library: solution briefs, white papers, reference architectures, case studies, web pages, webinars, and videos. Personally produce the most strategic pieces and set the bar for the rest. Elevate communications, analyst, and investor narratives. Shape the enterprise narrative for press moments, site launches, analyst briefings, and investor materials, in close coordination with comms, legal, and executive leadership. Serve as a spokesperson and subject matter expert for the enterprise story in analyst, customer, and industry settings. Champion the voice of the customer. Partner with sales and customer marketing to build a pipeline of references, case studies, and proof points, and bring enterprise pain points and wins back into product strategy and messaging. Mentor and multiply. Coach and guide other product marketers and cross-functional contributors, establish best practices, messaging methodologies, and workshop frameworks (such as SCIPAB), and raise the quality and consistency of product marketing across the organization. Contribute strategically at the leadership table. Bring ideas, challenge assumptions, and help shape how we position Nebius in a fast-moving AI infrastructure market. Act as a trusted partner to product, engineering, sales, and partnerships leaders, with a seat at the table, not just as a content source. We expect you to have: 8+ years in product marketing, with a strong track record leading enterprise go-to-market in cloud infrastructure or AI infrastructure (ideally the AI cloud market) at a growing, modern company. Proven ownership of the enterprise segment across the marketing lifecycle, with the ability to speak to win rates, pipeline data, and revenue impact on significant enterprise deals. Demonstrated experience setting go-to-market strategy and leading cross-functional launches across multiple products or product lines, influencing leaders without direct authority. Hands-on experience designing and orchestrating multi-stage enterprise customer journeys across long (6–18 month) buying cycles, and running account-based programs on ABM platforms such as 6sense or Demandbase in partnership with digital/ABX teams, with clear, measurable outcomes. Strong technical understanding of AI/ML workflows and the AI cloud infrastructure stack: GPU platforms and the NVIDIA ecosystem, networking, storage, IaaS, inference, and ML tools (e.g., PyTorch, Ray, Slurm, MLflow, Kubernetes, inference and agentic platforms), with the ability to go deep with engineers and translate up for executives and business buyers. Demonstrated experience building and running sales and partner enablement at scale, including battlecards, pitch decks, sales playbooks, competitive positioning, and ROI/TCO narratives, and driving measurable adoption across a field organization. Fluency with the enterprise buying process and its stakeholders (CTO, CIO, VP Infrastructure, IT Ops, Platform Engineering, security, compliance, procurement, and finance) and the concerns that gate a deal: TCO, reliability, security, compliance, sovereignty, and lock-in. Exceptional writing, presentation, and storytelling skills, including experience running messaging workshops and methodologies such as SCIPAB, and comfort presenting to C-level executives, analysts, and customers. Experience mentoring or leading product marketers or cross-functional teams, and raising standards across a function. Strategic thinker who thrives in a fast-paced, cross-functional environment, is comfortable operating with ambiguity and speed, and connects the dots across functions and departments. Bachelor’s degree in computer science, engineering, or a related technical field, or equivalent practical experience. #LI-LT1 Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $177,800 — $222,200 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Europe; New York; Toronto Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Key Overview: As a Member of Technical Staff in Post-Training, you will train our large language models (LLMs) with reinforcement learning and develop methods that help them generalize beyond the distribution they were trained on. You will design the training recipes, environments, and evaluations that tell us whether a model has truly learned a skill or has only fitted its training tasks, and turn what works into the recipes behind our production models. You will work closely with the post-training data infrastructure, evaluation, and pre-training teams. Key Responsibilities - Design, run, and analyse large-scale RL post-training experiments (e.g., RLHF, RL from verifiable rewards). - Research and develop methods that improve out-of-distribution generalization, such as reward design, environment and task diversity, curricula, and regularization. - Build held-out evaluations that measure generalization to unseen tasks, domains, and formats, and that catch reward hacking and benchmark overfitting. - Diagnose training dynamics and failure modes, including instability, reward over-optimization, and capability regressions. - Turn successful research into robust, reproducible training recipes for production models. Qualifications - Strong research and software engineering skills, with hands-on experience training LLMs. - Experience applying reinforcement learning to LLMs (e.g., PPO, GRPO, RLHF, RLVR), ideally at scale. - Track record of work on generalization, robustness, or distribution shift, through publications or shipped models. - Rigorous experimental practice: clean ablations, careful controls, and sound conclusions from noisy results. - Experience with distributed training (e.g., JAX, PyTorch, FSDP, Megatron) is a plus. - Excellent communication skills. - Deep understanding of data quality principles and best practices: demonstrates willingness to dive deep into data while possessing strong intuition for assessing the quality of datasets and anticipating how different data characteristics will impact model training outcomes. Working Location This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Задачи: — строить и оптимизировать масштабируемые системы обучения с подкреплением, конвейеры, инфраструктуру наблюдаемости и оценки. Требования: — опыт работы с reinforcement learning, machine learning. — знание Jax, Kubernetes, Grafana, Prometheus, Slurm, PyTorch, OpenTelemetry. Условия: — работа в офисе в Сан-Франциско, Калифорния. — зарплата $425,000 - $475,000 в год.