Вакансии

Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision. Возможен также classic ML;

Вакансия предполагает создание и внедрение нейросетевых моделей, которые объединяют методы компьютерного зрения (CV) и обработки естественного языка (NLP). Основная задача — разработка эффективных алгоритмов и моделей, способных работать на пересечении этих двух областей машинного обучения.

Разрабатывать и исследовать ML-модели для генерации изображений и визуализации интерьерных/строительных решений на основе товаров Лемана ПРО. Проектировать и реализовывать end-to-end пайплайны для PoC: от сбора и подготовки данных до обучения, оценки и демонстрации результатов.

Location: Cardiff, London or Remote (UK) 🚀 We’re on a mission to make money work for everyone. We’re waving goodbye to the complicated and confusing ways of traditional banking. After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts , accounts for 16-17 year olds , a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save , invest and combine their pensions with us. With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers! We’re not about selling products - we want to solve problems and change lives through Monzo ❤️ 📍London or Remote (UK)  | 💰  £85,000 - £105,000 + Incentive Awards tied to your performance + Benefits ⭐Our Model Oversight team The second line of defence Model Oversight team (that’s us!) provides support to our colleagues in Borrowing, Fincrime & Fraud, Finance and Operations to help Monzo grow safely. We’re looking for a Model Validation Senior Manager - Gen AI to lead the teams’ oversight of Generative AI models/use cases (primarily built across Monzo’s Operations squads) to provide assurance that the models/use cases we use are not only good for Monzo but what's best for our customers. 🔑You’ll play a key role by... Driving the embedding of the Model Risk Framework for Generative AI use cases across Monzo Lead the collaboration with the first line of defence teams to strengthen policies, standards and procedures for Generative AI model, use case, system design and build, implementation, governance, monitoring and use.  Helping to embed best practice of Generative AI across the business. Own the setting and maintenance of Generative AI validation methods, evidence expectations and reusable technical assets with relevant 1LoD modelling teams, applicable across all model risk areas. Convert modelling/validation experience into controlled playbooks, tests and working examples that can be applied to 2LoD Validation standards and processes for all Generative AI models, use cases, systems overseen by the 2LoD Model Oversight team. Coach Generative AI validation methodologies, to 2LoD Model Oversight colleagues, whom already have transferable Machine Learning skills, to reduce reliance on any single specialist for the validation of Generative AI models, use cases, systems so it can be consistently applied across Financial, Credit and Fincrime/Fraud models. Leading the 2LoD Oversight of Operational Model Risk Perform, and oversee, independent model validations of new Operational models (primarily Generative AI models, use cases, systems),  and changes to existing, using proportionate modelling techniques depending on the level of complexity applied. This includes writing clear and impactful model validation reports and presenting the findings in relevant committees. Build relationships with first line of defence modelling teams that develop Operational Models, and wider risk teams, to support the embedding of the model risk framework to help Monzo grow safely. Review ongoing model performance monitoring to ensure Operational models are performing within risk thresholds and providing support to the relevant model development team when further action is required Monitoring and measuring the overall level of Operational model risk that Monzo operates, and driving action plans with first line teams and appropriate governance forums to mitigate residual model risk within desired risk appetite. Being a senior advocate of Model Risk Management best practices Support the effective operation of the Model Risk Oversight Committee as a regular attendee, contributing to the group discussion to improve model risk management. Promote and help embed a strong risk culture, making risk meaningful and relevant to everyone at Monzo. You will share best practice that is easy for everyone to understand. 🤩We’d love to hear from you if… You are highly motivated and energised by the idea of bringing positive impact to our customers and Monzo You are self-motivated and thrive in a fast-paced environment You have great attention to detail while keeping an eye on the big picture You are a great communicator, able to articulate complex problems You can provide effective challenge whilst building strong, respectful relationships You are keen to grow your knowledge in both business and technology And, from a technical and knowledge perspective, you should have : Robust Data Science or Machine Learning background with strong Python capability Experience with design of Generative AI evaluation frameworks, architectures, methods, practical tooling and platform alignment. Knowledge of Gemini implementations and change assessment Experience of managing a modelling project through its full lifecycle If possible, some understanding of some of the following: Coaching and standard setting Banking MRM and regulatory context 🙌What’s in it for you 💰£85,000 - £105,000 ➕ Incentive Awards tied to your performance 📍This role can be based in our London or Cardiff offices, or be distributed working within the UK (with ad hoc meetings in London) ⏰We offer flexible working hours and trust you to work enough hours to do your job well, and at times that suit you and your team. 📚£1,000 learning budget each year to use on books, training courses and conferences. ➕ Plus lots more! Read our full list of benefits . 🌈 The application journey has 4 key steps: - Phone call with recruiter - 30 mins - Initial call with hiring manager - 30 mins - Assessment & technical interview - 60 mins - Leadership & Behavioural Interview - 60 mins This process should take around 4-8 weeks - your schedule is really important to us, so we promise to be as flexible as possible! We have some guidelines on using Artificial Intelligence (AI) to ace an application and interview at Monzo. You can read them here. #LI-TK1 Equal opportunities for everyone Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog , 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report. We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status. If you have a preferred name, please use it to apply. We don't need full or birth names at application stage 😊

- Сильный опыт в ML, Deep Learning, NLP и LLM. - Практический опыт создания AI-продуктов, работающих в production и в большом масштабе.

• 8+ лет опыта в прикладном ML/AI с ответственностью за системы end-to-end. • Уверенное владение Python.

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.

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: 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.

• 5+ лет опыта в Computer Vision и Deep Learning. • Глубокая экспертиза в Python и современных CV-фреймворках.

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: Paris ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. ABOUT THE ROLE As a Research Engineer, GPU performance, you will sit at the heart of that spectrum: your low-level work directly determines how fast our frontier models can be trained and served. Working hand-in-hand with Research Scientists, you'll make every GPU count. Mistral AI is hiring an expert in pushing large language model's GPU utilization and performance to their limits.. In this role, you will own the performance of our models end-to-end: writing the low-level code that extracts the maximum capacity from high-end hardware, and rethinking model architectures so they run efficiently at scale. WHAT YOU WILL DO - Develop low-level code that takes full advantage of high-end GPUs and maxes out their capacity — from custom kernels to memory-layout and communication optimisations. - Rethink parts of the generative model architecture to make them more suitable for efficient inference, in collaboration with Research Scientists. - Integrate low-level efficient code into a high-level MLOps framework, so that optimisations benefit every training run and deployment rather than a single experiment. - Profile, benchmark and squeeze performance out of large-scale training and inference on thousands of GPUs. - Model LLM execution on various GPUs with high fidelity to assess hardware usage and build serving performance estimates. WHAT WE'RE LOOKING FOR - High technical competence for writing custom low-level kernels and pushing GPUs to their limits. - High expertise in the distributed computation infrastructure of current-generation GPU clusters. - Overall understanding of the field of generative AI; knowledge of, or interest in, fine-tuning and using language models for applications. - Strong software-design instincts: testing, code review, CI/CD. - Self-starter, low-ego, collaborative. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: US, San Diego, CA 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 As an Autonomy Engineer focused in VLA Pre-training, you will work on all aspects of training capable policies. You'll pre-train base models on a diverse, multi-embodiment corpus of trajectories, fine-tune policies to excel at specific tasks, shape data collection processes, and explore effective ways to generate and use synthetic data. This is primarily a deep learning role, so we're looking for experience solving real-world problems with modern neural networks. Robotics experience isn't strictly required, but if you're coming from outside the field, be prepared to get up to speed on a new domain quickly.   WHAT YOU'LL DO - Post-train policies via behavior cloning and RL; own the full loop from data to deployment. - Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage. - Work closely with external partners to ensure steady supply of high-quality pretraining-scale data. - Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes. - Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining. - Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference. WHAT WE'RE LOOKING FOR - 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it. - Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference. - Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies. - Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code. - Familiarity with modern software engineering practices. - You document experiments clearly and communicate trade‑offs crisply. NICE TO HAVE - Robotics or autonomous driving experience. - Experience applying RL to LLMs or robotics. - Experience with VLA (vision-language-action) models. - Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization). - Publications at top-tier deep learning conferences or equivalent open‑source contributions. - Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open source VLA frameworks. WHAT WE OFFER - Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources. - Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays. - 401(k) retirement plan with employer match. - Equity included–we believe builders should share in what they build. - Free daily catered lunch, snacks, and drinks in‑office. - Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics. - Freedom to influence the product and own key initiatives. For this role in California, the expected base salary range is $180,000–$300,000 USD per year; your placement in that range depends on how your experience maps to our internal leveling.

Задачи: — создавать AI-агентов и платформы для них; — строить MLOps-инфраструктуру; — работать с данными для ML. Требования: — опыт работы в роли MLOps Engineer, Data Engineer или Python ML от 3,5 лет; — production-опыт с фреймворками MLOps: Kubeflow, MLflow, Airflow для оркестрации, понимание их интеграции в CI/CD пайплайны; — опыт работы с Docker и Kubernetes (в контексте развертывания моделей как сервисов); — опыт работы с одним из major cloud-провайдеров (желательно GCP или AWS/Azure с пониманием hybrid-cloud); — умение работать с большими данными, опыт с Greenplum или аналогичными MPP-системами (Teradata, Exasol); — знание распределенных вычислений (Spark); — опыт работы с LangChain/LlamaIndex, FastAPI, знание фреймворков для работы с LLM (Hugging Face, vLLM и т.д.); — отличные знания Python, SQL на очень высоком уровне для работы с data vault; — знания Git, GitLab CI/CD. Условия: — хорошая зарплата, уровень обсуждается индивидуально, есть доплаты за менторство и профактивности; — долгосрочные проекты от российских заказчиков, возможность менять направления, прозрачная система Performance Review; — релокейт между офисами, выбор формата работы (удалённо, офис, гибрид), адаптация для новых сотрудников; — доступ к корпоративному порталу, митапам, конференциям (и как гость, и как спикер); — социальный пакет: ДМС со стоматологией, частичная компенсация спорта, бесплатный английский, оплачиваемый отпуск и больничные; — корпоративная жизнь: тимбилдинги, детские праздники, внутренние мероприятия; — офисы в Москве, Санкт-Петербурге, Казани, Нижнем Новгороде, Самаре.

Задачи: — Разрабатывать модели прогнозирования, скоринга, детекции аномалий и AI-агентов. — Выводить модели в production и масштабировать их. — Руководить небольшой ML-командой: менторинг, процессы, распределение задач. — Работать напрямую с заказчиками и участвовать в R&D. Требования: — Strong Senior в ML/AI. — Python, статистика, PyTorch/TensorFlow, scikit-learn, MLflow. — Опыт работы с AWS/Azure/GCP. — Системное мышление и желание строить технологии, которые влияют на решения национального уровня. — Команда русскоязычная, знание английского - плюс.

Требования: — Опыт применения Generative AI и LLM к задачам discovery Важно: — Только для кандидатов в Северной Америке

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.