Вакансии

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.

Location: Munich; Warsaw; London Workplace: Hybrid 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. THE ROLE The Inference Foundation team owns the core of Mistral's inference stack: the inference engine and its orchestration, from the feature set and configuration that serve our models in production to the release machinery that keeps the stack current and production-grade. This is a hybrid position spanning production LLM serving, engine and platform development, and capacity engineering. You will work on three intertwined problems: 1. Optimize the inference stack at scale — feature development and fixes in the engine and orchestrator, squeezing more throughput and lower latency out of every GPU under strict quality-of-service targets. 2. Make capacity elastic — scaling up and down should be cheap and fast, not a performance cliff. 3. Power the training of our frontier models — high-performance serving that keeps RL and post-training loops running at full speed.   What You Will Do INFERENCE ENGINE & ORCHESTRATION - Develop and fix the core of the inference stack — engine and orchestrator — including feature selection, configuration, and tuning for maximum performance at scale - Own the release process for the serving stack: validated, regression-free releases through automated performance gates and progressive rollout - Drive improvements and fixes upstream when the open-source engine is the right place for them PERFORMANCE & CAPACITY AT SCALE - Optimize serving efficiency across the fleet — driving down pod startup time, tackling cold-cache regressions on scale-up, smarter caching and offloading - Optimize and maintain the optimal serving topology — overlap communication and transfers with computation, ensure optimal placement, connectivity, and routing SERVING FOR FRONTIER TRAINING - Build the serving infrastructure that powers RL and post-training for our frontier models - Optimize inference performance across the full spectrum of our workloads   What We're Looking For - Experience building and running ML/LLM services at scale, with clear latency and availability targets - Hands-on experience with inference engines such as vLLM, SGLang, TensorRT-LLM, or others - A solid grasp of inference internals: prefill vs. decode, KV-cache behavior, batching, scheduling, speculative decoding, parallelism strategies - Familiarity with distributed and disaggregated serving architectures - Comfortable debugging across the full stack — CUDA/NCCL, kernels, containers, networking, storage - Python for systems tooling and backend services; PyTorch - Kubernetes for running infrastructure at scale - GPU and networking fundamentals: CUDA runtime, NCCL, InfiniBand/RDMA It Would Be Great If You Have - Demonstrated vLLM/sglang know-how — upstream contributions, or a track record of running in demanding production environments - Hardware-aware optimization for various model architectures - Experience serving MoE models at scale (expert parallelism, expert placement/load balancing) - CUDA/Triton kernel development; Nsight Systems/Compute profiling - Rust and/or C++ in production systems 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.

В рамках проекта по созданию платформы интегрированного планирования требуется инженер машинного обучения для разработки модулей, связанных с планированием снабжения и производства. В обязанности входит проектирование и реализация компонентов для календарного и производственного планирования, а также оптимизации запасов. Необходимо формализовать бизнес-задачи в виде математических моделей оптимизации, включая линейное программирование (LP) и смешанно-целочисленное линейное программирование (MILP). Кандидат должен обладать глубокими знаниями алгоритмов и структур данных, владеть Python на уровне Senior, уметь применять шаблоны ООП и проектировать сложные вычислительные решения. Требуется опыт работы с SQL, PostgreSQL, Git, а также навыки юнит- и интеграционного тестирования. Опыт в области Operations Research, систем планирования производства (APS), логистики или управления цепочками поставок обязателен, как и практический опыт построения моделей дискретной и линейной оптимизации для реальных задач. Дополнительным преимуществом будет знание инструментов оптимизации (Pyomo, OR-Tools, PuLP, COIN-OR), решателей (Gurobi, CPLEX, SCIP, COPT, GLPK), математической статистики, методов дискретной оптимизации и программирования на C/C++ для оптимизации вычислительных узлов. Также приветствуется опыт работы в Unix-подобных системах, понимание принципов CI/CD, умение декомпозировать крупномасштабные задачи оптимизации, а также наличие высшего технического или математического образования и специализированных курсов по дискретной оптимизации, Operations Research или алгоритмам. Проект рассчитан на 3 месяца с возможностью продления, работа удалённая, гражданство РФ обязательно, форма занятости — ГПХ или ИП.

Location: San Francisco, CA Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Sierra was co-founded by Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/ who first met while working together at Google over 20 years ago. Bret currently serves as Chairman of the Board of Open AI. Previously, he was co-CEO of Salesforce and CTO of Facebook. Clay spent 18 years at Google, where he most recently led Google Labs. Sierra’s Applied Research team advances the quality, speed, and cost efficiency of the models powering our agents across a wide range of tasks. Our work spans post-training open-weight models for specialized capabilities and pioneering new approaches to voice AI, turning research breakthroughs into reliable production systems. WHAT YOU’LL DO - Tackle unsolved applied AI and ML problems. Pushing the boundaries of post-training, voice AI, and long-horizon task execution, building agents that can reason, adapt, and reliably carry out complex tasks for customers. - Determine when to build specialized models and when to use externally provided frontier intelligence, balancing quality, speed, and cost to deliver customer value. - Turn ambiguous product and customer problems into well-framed machine learning problems. - Develop, fine-tune, evaluate, and deploy models that improve Sierra’s customer-facing AI agents. - Advance our post-training work, from supervised fine-tuning toward reinforcement learning and long-horizon optimization. - Build rigorous evaluations that connect model performance to real-world agent quality. - Work with large-scale, proprietary datasets spanning text and audio interactions. - Collaborate across Sierra while taking ideas from experimentation through production deployment and customer impact. WHAT YOU’LL BRING - Strong experience building and deploying production machine learning and AI systems. We welcome backgrounds across applied ML, including search, ranking, recommendations, advertising, language, and speech. - Excellent judgment in framing product problems as ML tasks and selecting the simplest & most effective approach. - Experience working deeply with data, experimentation, evaluation, and model iteration. - A practical, evidence-driven approach to ML, with strong judgement around when a problem calls for modeling and when a simpler solution will deliver the best outcome. - The ability to operate with high agency and work across the stack when needed to deliver an outcome. - Alignment with Sierra’s values, including Craftsmanship, Customer Obsession, and Competitive Intensity. - A degree in computer science, machine learning, or a related field, or equivalent professional experience. EVEN BETTER… - Experience with supervised fine-tuning or reinforcement learning. - Experience in search, ranking, recommendations, or advertising systems. - Experience training and deploying language, speech, or multimodal models. - Experience designing model evaluations tied to real-world product outcomes. - Experience working with large-scale consumer datasets or high-volume ML products. - Contributions to applied ML research or benchmarks. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits - Lunch, as well as delicious snacks and coffee to keep you energized when working onsite - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region and employment type, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply through the link on this page even if your experience doesn't precisely match the job description. Sierra is a proud equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any characteristic protected by applicable federal, state or local laws, including veteran status and disability status. Hiring decisions are made by people, based on merit. If you need a reasonable accommodation for a disability, a religious practice, pregnancy or any related condition at any point in the application or interview process, contact us at accommodations@sierra.ai. Requests are handled confidentially and do not affect your candidacy. We will consider qualified applicants with arrest and conviction records in accordance with applicable fair chance laws.

Задачи: — управлять командой, состоящей из Data Scientist, Data Engineer, MLOps — соблюдать требования и стандарты производственного процесса банка — взаимодействовать с руководителями проектов и внутренними заказчиками — участвовать в архитектурных решениях, касающихся внедрения моделей и данных для моделирования — оценивать трудозатраты и сроки, выполнять декомпозицию задач для команды — контролировать качество и сроки выполнения задач — выполнять Code Review, участвовать в приемо-сдаточных испытаниях Требования: — опыт работы в роли MLOps Engineer от 5 лет с желанием расти в лидерскую позицию или опыт работы в роли Лидера команды/ИТ-лидера команды от 2 лет и опыт работы в команде в роли Data Engineer от 3 лет — опыт разработки на Python — знания в области Big Data (Hadoop, Spark) — опыт работы с базами данных, уверенные знания SQL — понимание гибких методологий разработки (Agile) — понимание принципов CI/CD, Gitflow, управления релизами Будет плюсом: — знание планировщиков процессов (AirFlow) — знание технологий контейнеризации (Kubernetes) — опыт разработки и/или внедрения ML моделей в бизнес-процесс — понимание способов интеграций IT-систем Условия: — уровень зарплаты 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)

Задачи: — Управление Cost of Risk по всем кредитным продуктам. — Создание и развитие моделей скоринга, поведенческого анализа и кредитных лимитов — от гипотезы до внедрения в продакшен. — Автоматизация программы повышения кредитного лимита с переходом от ручных правил к машинному обучению. — Контроль процессов взыскания на ранних и поздних стадиях, включая восстановление задолженности. — Разработка методологии резервирования совместно с финансовым отделом. — Подготовка ежемесячных отчетов по рискам для CEO и совета директоров. — Установление стандартов валидации моделей, мониторинга и регулярного переобучения. Требования: — Опыт работы 6+ лет в области кредитного риска по необеспеченным потребительским кредитам, включая 2+ года на руководящей должности. — Практический опыт создания моделей скоринга, поведенческого анализа и кредитных лимитов. — Прямое управление показателем Cost of Risk / NPL как ключевым метрикой эффективности. — Глубокое понимание процессов взыскания как части общей кредитной экономики. — Опыт работы с распределенными командами. — Профессиональный уровень английского языка. — Русский язык является плюсом. Желательно: — Опыт работы с картовыми программами и автоматизированным управлением кредитными лимитами. — Опыт работы с BNPL / рассрочкой. — Опыт внедрения ML-моделей в продакшен. — Опыт работы в регулируемом банке. — Опыт работы в Латинской Америке и знание испанского языка.

Location: Kyiv; Lviv Workplace: onsite Ajax Systems, an international tech company and the largest manufacturer of security systems in Europe.We are looking for a new team member to join our Video Devices Division as a Computer Vision Engineer . The Video Devices Division is responsible for the full development cycle of Ajax video devices, covering every stage from initial concept creation to mass production launch and further scaling. The team focuses on building complex products that combine mechanical design, hardware development, and the integration of low-level software to ensure stable real-time system performance.The development process involves deep expertise in optics, image quality, embedded systems, Wi-Fi and networking technologies, as well as audio tuning. The division ensures full vertical integration of all device components, creating technologically complete solutions that meet the reliability standards of the Ajax ecosystem and the requirements of global industrial production. What you'll work with: object detection; object tracking; face recognition; multi-modal search; acoustic intelligence; models optimization for edge inference; research and deliver sota computer vision solutions to exceed today's cctv domain expectations. Your cross-functional partners: ML team; platform team; annotation team; QA team. What we're looking for: 5+ years with machine learning; 2+ years with computer vision and python; experience with PyTorch ot TensorFlow. Nice to have: experience with C++; deploy AI models on edge devices; computer vision analytics troubleshooting. We offer: -the opportunity to suggest and implement your own ideas that directly influence the product and its mass production; - a place in a fast-growing company with a motivated and professional team; - competitive salary; - medical insurance; - corporate English classes; - the opportunity to participate in Ajax’s internal beta-testing program for security systems; - discounts from partner companies; - discounts on Ajax products. Ajax Systems is a Ukrainian success story that has grown into a global product. Ajax devices are trusted by more than 4.5 million end users and 330,000 PRO users in over 180 countries. The company provides comprehensive solutions to protect residential and commercial properties of all sizes. Today, Ajax portfolio includes 280 devices for intrusion protection, video surveillance, fire safety, and comfort and automation.

Location: Berlin ROLE HIGHLIGHTS: - Senior MLOps Engineer, ISR - Location: Berlin, Germany - Department: ISR Analytics - Reports to: VP of ISR Analytics - Employment type: Permanent - Workplace model: Hybrid - Employment is subject to applicable security screening (incl. SUPO, where required) WHY THIS ROLE MATTERS: ICEYE's Intelligence, Surveillance and Reconnaissance (ISR) AI team builds models that turn satellite imagery into intelligence for defense customers. Your job is to work with data scientists to build sustainable infrastructure, workflows, and deployment practices for the AI models, both in the cloud and for on-premise systems. You sit with the AI team and work with them to build the infrastructure they train, evaluate and ship on. Like our platform work, every environment is temporary and must be reproducible from code; a model is only done when it runs, monitored, on hardware we don't control.   WHO WE ARE ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth.   ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system.   ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future.   Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific. YOUR DAY-TO-DAY RESPONSIBILITIES   - Build training, evaluation and packaging pipelines that take a model from experiment to versioned, deployable release. - Deploy and serve models on Kubernetes with GPUs, in the cloud and on-prem, including air-gapped sites. - Package models, weights and dependencies so they install and upgrade offline. - Manage GPU compute: scheduling, drivers, utilization and cost, across cloud and on-prem hardware. - Track data, experiments and model lineage, so any result can be reproduced and audited. - Monitor models in production for performance, drift and failures, without relying on outside connectivity. - Write tooling in Python that the AI team uses every day, and work in the model codebase alongside them.   WHAT WE’RE LOOKING FOR Must haves: - Senior hands-on engineer (not an engineering manager or architect role). - Proven experience deploying ML models to production and running them there, not only training them. - Strong Python, and comfort reading and changing ML code (PyTorch or similar). - Hands-on Kubernetes and containers, including GPU workloads. - Infrastructure as code and CI/CD for ML (for example Terraform, Helm, GitHub Actions). - Daily use of AI tools in engineering work, beyond chat: generating and reviewing code, configuration and tests. - High autonomy: you find problems, propose fixes and drive them through. - Motivation to work in new defense: building technology used by defense forces.   Nice to haves: - Deployed models to on-prem, edge or air-gapped environments. - Model serving and optimization: Triton, TorchServe, KServe, ONNX, TensorRT, quantization. - Computer vision or geospatial data (satellite, SAR, raster, PostGIS). - ML pipeline and tracking tools such as MLflow, Kubeflow, Argo Workflows, DVC or Weights & Biases. - Large-scale data processing for imagery (Dask, Ray, Spark, object storage). - Strong IC track record in a startup or a fast-changing company launching new products. - Clear communicator who works well with researchers and experienced engineers.   WORKING AT ICEYE At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win. HOW WE WORK (OUR VALUES) Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way. - Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions. - See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others. - Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions. - Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift. - Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins. BENEFITS Our benefits are designed to support your health and wellbeing, at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process.   OUR COMMITMENT TO DIVERSITY, EQUITY, AND INCLUSION We want ICEYE to be a place where people can be themselves and do great work. Different backgrounds and perspectives make us stronger, which is why we work to create an environment where people feel included, respected, and able to speak up. Whatever your background, we want you to bring your authentic self to the table. We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help. Apply now to start your ICEYE journey, and help us continue to make the impossible possible together. Read more about ICEYE and working with us at iceye.com http://iceye.com.

Задачи: — Техническое развитие направления: формирование технического курса, выбор ключевых гипотез, принятие решений по результатам экспериментов. — Обучение и управляемость модели: развитие методов обучения, данных и reward-моделей для следования системным инструкциям. — Оценка качества: формулирование требований к оценке, контроль качества и проверка отражения изменений в поведении модели. — Интеграция результатов: доведение решений до релизной модели, согласование технических изменений, участие в разборе сложных проблем. — Техническая работа с командой: исследование подходов к управляемости языковых моделей, проектирование решений для проверки на Alice AI. Требования: — Хорошее понимание устройства современных LLM, instruction tuning и методов алайнмента. — Опыт руководства техническим развитием ML-направления или сложного исследовательского проекта. — Умение связывать продуктовые цели, исследовательские гипотезы и метрики качества. — Опыт построения воспроизводимого цикла ML-экспериментов и принятия технических решений по их результатам. — Навыки проектирования ML-систем и оценки применимости новых подходов к задачам продукта. Будет плюсом: — Опыт работы с instruction following или controllability языковых моделей. — Опыт работы с оценкой поведения LLM или reward-моделями. Условия: — Ежегодные медицинские чекапы, йога и психотерапия. — Другие бонусы, полный список доступен по ссылке https://yandex.ru/jobs/pages/benefits.

Задачи: — Разработка и обучение моделей для анализа изображений и облаков точек — Подготовка датасетов, анализ данных — Сбор, анализ и предобработка данных, составление датасетов — Обучение моделей и тестирование гипотез — Деплой полученных решений — Написание технической документации — Презентация результатов внутри команды и заказчику — Изучение новых подходов для решения поставленных задач — Взаимодействие с командой разработки и аналитиками Требования: — Опыт разработки ML-решений для бизнеса от 3 лет — Опыт работы с CV на реальных проектах от 1 года — Опыт промышленной разработки на Python от 3 лет — Умение писать production-ready код на Python — Опыт работы с PyTorch — Умение обучать и дообучать модели, проводить тестирование — Опыт работы с Git — Умение версионировать данные и логировать эксперименты (DVC, MLFlow, ClearML) — Опыт работы с Docker — Опыт проработки требований, опыт декомпозиции и приоритизации задач — Умение вести техническую документацию — Знание английского B2 — Опыт работы с трехмерными данными Будет плюсом: — Опыт работы с ГИС и специальными форматами данных (geotiff, las)

Задачи: — Проектировать, создавать и внедрять модели машинного обучения для прогнозирования ключевых бизнес-показателей, таких как отток, расширение, конверсия и вовлеченность клиентов. — Разрабатывать и поддерживать масштабируемые ML-пайплайны для непрерывного приема данных, переобучения моделей, проверки производительности и предоставления предсказаний в продакшене. — Работать с большими структурированными и неструктурированными данными, применяя современные методы машинного обучения и большие языковые модели (LLM), если они приносят измеримую бизнес-ценность. — Мониторить модели в продакшене, улучшать их производительность со временем, обеспечивать надежность через тестирование, валидацию и непрерывную итерацию. — Сотрудничать с инженерами данных, менеджерами продуктов и бизнес-стейкхолдерами для создания ML-решений, решающих реальные задачи клиентов и бизнеса. — Владеть ML-решениями на всех этапах — от определения проблемы и экспериментов до внедрения в продакшен, мониторинга и оптимизации. Требования: — 3–5 лет профессионального опыта в области машинного обучения, прикладного машинного обучения, науки о данных или смежных областях. — Степень бакалавра в области компьютерных наук, математики, статистики, инженерии или другой количественной дисциплины. — Магистратура или PhD приветствуются, но не обязательны. — Глубокое понимание методов машинного обучения и статистического моделирования, включая регрессию, классификацию, анализ выживаемости, причинно-следственный анализ, uplift-моделирование и другие. — Опыт разработки моделей на больших реальных данных, включая инженеринг признаков, валидацию моделей и работу с несовершенными данными. — Опыт работы с неструктурированными данными с использованием эмбеддингов, дообучения, векторных представлений и современных NLP-техник. — Сильные навыки программирования на Python с опытом создания программного обеспечения промышленного уровня. — Уверенные навыки SQL и опыт работы с облачными хранилищами данных (предпочтительно Snowflake). — Опыт развертывания, обслуживания и мониторинга моделей машинного обучения в продакшене. — Знание дизайна экспериментов, A/B тестирования или причинно-следственного анализа будет плюсом. — Опыт работы с инструментами оркестрации рабочих процессов, такими как Airflow, dbt или аналогичными, приветствуется. — Умение использовать AI-инструменты для разработки, например Claude Code, Cursor, GitHub Copilot и др. Условия: — Полная занятость. — Удаленная работа из Кракова, Польша. — Годовой базовый оклад от 98 000 до 146 000 евро. — Возможность получения бонусов, льгот и других стимулов, которые обсуждаются на этапе предложения. Дополнительно: — Работа в команде Enterprise Machine Learning, которая создает интеллектуальные системы для улучшения бизнес-решений. — Взаимодействие с продуктовой, инженерной и бизнес-командами. — Компания придерживается принципов равных возможностей и инклюзивности. — Использование AI и автоматизированных систем для оценки заявок в соответствии с законодательством. — Возможность получения разумных приспособлений для соискателей с инвалидностью.

Responsibilities: — Develop and adapt computer vision/video analytics models for detection, tracking, and recognition. — Work with video pipelines, including RTSP/HTTP, decoding, sampling, and batching. — Fine-tune models for real-world conditions and customer scenarios. — Optimize CPU inference, latency, and resource usage. — Integrate models into backend, edge, and on-premise solutions. — Prepare datasets, build training/testing pipelines, and monitor quality. Requirements: — 4+ years in Computer Vision/Video Analytics. — Strong CNN, YOLO, and SSD knowledge. — PyTorch or TensorFlow, OpenCV, and ONNX. — Understanding of RTSP, H.264/H.265, FPS, bitrate, and latency. — CPU inference optimization experience. — Linux and OpenVINO. — C++ for performance-critical components.

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . About the Role Reddit’s Ads Creative Effectiveness team sits at the heart of our creative strategy, building GenAI and predictive products that help advertisers ship high-impact campaigns. We are searching for a Staff engineer with in-depth GenAI expertise that can help us build the future of generative products for ad creative at Reddit - best-in-class image, video and copy generators that leverage Reddit’s best practices, performance-guided generations that maximize clicks and conversions, and brand safe, product preserving image/video editing pipelines. What you’ll do: Advanced Generative Workflows: Architect and implement state-of-the-art pipelines for ad creative generation. You will move beyond vanilla prompting to build systems capable of product, font, logo and brand asset preservation using techniques like IP-Adapters, and LoRAs. End-to-End Image & Video Editing: Build editing capabilities allowing advertisers to smart-crop, inpaint, outpaint, resize, and style-transfer assets while maintaining strict brand guidelines. LLM/VLM Fine-Tuning: Adapt large vision-language models to understand "winning" ad aesthetics. You will build feedback loops where ad performance metrics (CTR, CVR) are used to fine-tune the generation process (RLHF/RLPF for Ads). Safety & Compliance Layer: engineer robust filtering and safety systems. You ensure our models never generate non-compliant, unsafe, or copyright-infringing content. Partner Broadly: Collaborate with Product, Sales, Policy, UX, and sibling Ads ML teams to integrate ACE capabilities into the Ad Manager UI and the ad delivery path. Guardrails & Safety: Embed brand-safety filters, copyright checks, and human-in-the-loop escalation paths to protect users and advertisers. What we’re looking for: Deep ML Expertise: Hands-on experience with tree models, neural nets, ranking, and especially LLM/VLM fine-tuning & RLHF/RLPF for ads. Quant & Experimentation Chops: Comfort with uplift metrics, variance, minimum detectable effects and experimentation setups for generative systems. Ads / Marketplace Insight: Understanding auction dynamics and advertiser pain points to create tools that drive revenue and retention. Startup Mindset: Bias to action, comfort with ambiguity, and scrappiness: ACE evolved from a startup acquisition and still moves fast. Communication Superpowers: Able to distill complex ML trade-offs for executives, product managers, and non-technical stakeholders alike. Prior work on ad creative tooling: Headline generators, image generators, pre-test models, editing flows for the advertiser. Nice-to-have Experience navigating brand-safety, privacy, or copyright frameworks in generative AI products. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $230,000 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: San Francisco, CA 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 Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI. We're looking for a Staff Engineer, Robot Learning to lead engineering for our real-to-sim product within the Embodied AI group. One of the fundamental problems facing autonomy teams is the sim-to-real gap in visual-spatial understanding, and we're applying decades of experience encoding the world precisely as it is to build the scalable real-to-sim stack for embodied AI. Working alongside the group's General Manager and Product leader, you'll inform the roadmap, own the technical requirements our researchers and engineers build against, and personally prototype and evaluate the changes you propose. You've lived the problems our customers are solving: you've trained and deployed embodied AI systems, seen where and why simulation fails to predict real-world performance, and worked through the consequences. This is not a role for someone who wants to manage and not ship, or someone who wants to ship without ownership of commercial consequences. This is a role for an entrepreneurial tech lead who loves to build and is energized by helping those around them do the best work of their lives. You have a co-founder mentality. You may have been part of a founding team before, or want to start your own business one day. What You'll Do - Sharpen the Roadmap - Bring first-hand knowledge of customer pain, existing solutions, and the value of doing better to roadmap decisions, and help focus effort on the most consequential problems to solve. - Own the Technical Requirements - Guide the team to design the real-to-sim stack pragmatically. Make the final recommendation on what to build, integrate, and simplify, ensuring capabilities work within customers' existing training and evaluation workflows. - Benchmark the Impact - Working with our Real-World Test Lab, design and run reproducible evaluations that measure the impact of our real-to-sim stack on policy transfer and real-world performance against customers' existing approaches. - Build and Ship - Lead engineering delivery and personally build critical features. Keep scope tight, remove blockers, and get performant capabilities into customers' hands quickly. - Raise the Technical Bar - Set a high bar for the team's work and deepen its understanding of robotics development and deployment, driving incremental improvement every week. What You'll Bring - Experience training, evaluating, and deploying robot policies in systems used by enterprise or government customers, with responsibility for performance in real operating conditions. - Deep practical experience using simulation for policy training or evaluation, including tools such as NVIDIA Isaac Sim, Isaac Lab, MuJoCo, or equivalent. You have diagnosed sim-to-real failures and understand when greater fidelity improves outcomes. - Hands-on experience with reinforcement learning, imitation learning, or both, and with deep-learning frameworks such as PyTorch. - A practical understanding of how visual perception and 3D scene representations affect robotics training, evaluation, and real-world performance. - Experience running experiments on real robots, from an ambiguous hypothesis through to defensible results. - Strong Python and C++ skills, with experience building reliable production software. - A bachelor's degree or higher in a relevant field. Nice to Have - You've authored or maintained a public robotics benchmark. - You've helped take an early product to repeatable adoption across multiple customers. - You've built or substantially extended robotics simulation infrastructure, including scene authoring, domain randomization, or large-scale training and evaluation. - You've trained and deployed vision-based navigation or mobility policies, particularly in varied, unstructured environments. - You've built pipelines that turn real-world reconstructions into simulation-ready environments. Competencies - Always Learning. Staying on top of the latest academic breakthroughs and commercial advancements, particularly in real-to-sim-to-real. - Relentless bias for action. You make decisions and ship as if the company’s success depends on it, because it does. You set the pace, and drive the people around you to rise to it. - Intellectually honest. You know there’s just one job that underpins every other in a startup: find the truth. You are relentless in your pursuit of it, including when it challenges your own convictions, and you raise concerns when you see them — even when it's uncomfortable, and especially when it's unpopular. - Pragmatic. You have no patience for not-invented-here syndrome and analysis paralysis. You find the fastest path to delivering customer value without going through one-way doors that undermine future scaling. - Confident and credible with customers. You can sit alongside an autonomy engineer and help them get to the root of their problems. You are comfortable speaking to customers and bring commercial nous to those discussions. Compensation & Benefits The expected salary range for this role is $270,000 – $300,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco 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: Berlin adjoe builds the technologies behind mobile app growth and monetization, challenging the status quo of adtech by redefining how apps acquire users and generate revenue. Our two products, Playtime Arcade and adjoe Ads, sit at opposite ends of the mobile advertising spectrum: rewarded engagement and programmatic in-app advertising. Together they run on 13% of the world’s smartphones, connecting advertisers, publishers, and hundreds of millions of users, with one goal: making mobile advertising work better for everyone in the ecosystem. Next-Gen Machine Intelligence for Auctions & Real-Time Bidding The Data Science Research team builds the foundational algorithms that govern real-time bidding, budget pacing, and allocation mechanics across our products. Operating as a horizontal deep-tech layer, the team leverages advanced AI—specifically reinforcement learning, objective-based optimization, and control theory—to deliver critical systems that perfectly harmonize advertiser ROI, platform health, and user relevance. These frameworks scale adjoe’s products beyond standard industry limitations, utilizing behavioral and transactional data from over 770 million users. The team operates at the intersection of high-load, low-latency engineering, auction mechanism design, and state-of-the-art machine learning. Acting as the company’s internal science bar raiser, they translate complex business constraints into mathematical objectives. The stack is optimized for high throughput: PyTorch for training deep learning and RL models, distributed architectures for large-scale Bayesian inference, Trino and Athena for querying massive user-behavior data lakes, and dedicated GPU infrastructure. The full lifecycle of these AI capabilities is owned by the team—from academic paper translation and offline prototyping to deploying core algorithmic frameworks built to sustain ultra-low latency, real-time production environments evaluating tens of thousands of requests per second. Your Mission & Who We Are Looking For: - PhD or equivalent research depth, with a production track record. You have a PhD in CS, Statistics, Math, or Physics (or an MSc with extensive industry research) and a track record of peer-reviewed publications in fields like Computational Advertising, Auction Theory, Mechanism Design, or Game Theory. You’ve spent 4+ years optimizing complex systems through automated algorithms in AdTech, marketplaces, or high-frequency trading. - You translate research into production, not just prototypes. You distill SOTA research into robust, production-ready code optimized for sub-millisecond latency. At adjoe, that means architecting sophisticated auction mechanics, real-time bidding engines, and allocation algorithms serving millions of requests across a stack built on 770 million users. - Deep expertise in Reinforcement Learning and Optimization. You have hands-on, deep theoretical and technical expertise in objective-based optimization, control theory, or RL applied specifically to bidding, pricing strategy, and budget pacing under extreme production constraints. - Mastering high-dimensional data at scale. Large-scale Bayesian inference and handling sparse, high-dimensional advertising data are core to your work. You are strongly proficient in Python and related stacks, and know exactly how to architect scalable data pipelines to feed real-time optimization models. - Raises the technical bar around them. You lead architectural debates, collaborate tightly with Product Managers and Engineers to map business goals to mathematical objective functions, mentor junior scientists, and drive the team’s research direction without needing a roadmap handed to you. 🌎 WE WELCOME APPLICATIONS FROM TALENT WORLDWIDE AND PROVIDE RELOCATION SUPPORT TO HAMBURG, GERMANY FOR THOSE READY TO JOIN OUR TEAM. What’s in It for You? At adjoe, you’re not here to just close JIRA tickets, you’re helping build the infrastructure behind one of the most impactful platforms in adtech. The systems you work on will reach hundreds of millions of users and power billions of decisions every day. - Go Big. Own projects with impact on 770M users and push adtech boundaries. - Move Fast. Ship solutions multiple times a day, learn from results, and keep momentum. - Be Direct. Solve problems openly and collaborate across teams. - Thrive Together. Grow with a diverse, global team of people from over 40 different countries that learn from each other. - Have Fun. Celebrate wins, enjoy daily victories, and bring your energy. We welcome applications from people who will contribute to the diversity of our company.

Location: Москва Мы — core-команда, которая отвечает за ML для понимания аудио во всем Сбере. За последний год мы выложили в open source SOTA-модель для распознавания речи на русском языке — **GigaAM**, а прошлой весной первыми в России запустили **нативное понимание звука в LLM — GigaChat Audio**. Сейчас мы активно улучшаем мультимодальные возможности GigaChat: работаем над качеством на сложных аудио- и визуальных контекстах и учим модель понимать видео не только по звуковому потоку, но и по кадрам. **Цель** — совместное обучение на vision+audio и video+audio диалогах длительностью 90+ минут. _Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram. Диалог займёт примерно 10 минут. Его задача — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным!_ Обязанности * Проводить эксперименты по обучению Vision / Audio / Video LLM: данные, стадии обучения, способы смешивания модальностей * Оптимизировать ML-инфраструктуру для обучения на длинных мультимодальных контекстах * Формулировать и проверять гипотезы о том, как улучшать качество LLM на видео. Требования * Сильный опыт в DL-инженерии, желательно в обучении LLM-моделей * Глубокое знание PyTorch, включая DDP / FSDP * Понимание distributed training и efficient deep learning: DP / TP / PP / EP / SP, mixed precision, checkpointing, offloading, профилирование и оптимизация обучения * Понимание архитектуры LLM: Transformer, attention (MHA / GQA / MLA), RoPE и другие позиционные эмбеддинги, long context, MoE * Уверенный Python на уровне production-кода: asyncio, multiprocessing, профилирование, отладка больших систем. **Будет плюсом:** * Опыт обучения мультимодальных LLM * Опыт снижения стоимости контекста при обработке видео * Опыт расширения контекста LLM * Понимание RL-подходов для обучения моделей: RLHF / RLVR, PPO / GRPO / DPO * Опыт построения бенчмарков с использованием LLM-as-a-judge. Условия * крупнейшее DS&AI community — более 600 DS-специалистов банка * дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира * возможность быть соавтором НИРов и статей для международных конференций * возможность выбрать удобный формат работы: гибрид или офис * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * ипотека выгоднее до 7% для каждого сотрудника * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Мы ищем сильного Deep Learning инженера для развития и вывода на принципиально новый уровень GigaAM — лучшей open-source модели для распознавания речи на русском языке. GigaAM — это не только исследовательский проект, но и core-модель, которая определяет качество работы многих ключевых продуктов экосистемы: GigaChat Audio, ASR, TTS, GigaChat Video и других. Сейчас мы стоим перед масштабным вызовом: кратное увеличение параметров модели, масштабирование данных на несколько порядков, расширение языкового покрытия и выход за рамки распознавания речи в сторону general audio understanding. Если вы хотите создавать state-of-the-art решения, которыми будут пользоваться миллионы, и вносить вклад в развитие мирового open-source — нам по пути! _Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram. Диалог займёт примерно 10 минут. Его задача — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным!_ Обязанности * Масштабирование данных: увеличение объемов данных для обучения на несколько порядков, построение эффективных пайплайнов их фильтрации и подготовки. * Развитие архитектуры и метода предобучения: улучшение мультиязычных свойств модели и расширение ее возможностей в сторону анализа аудио неречевой природы. * Scaling laws: масштабирование модели по количеству параметров в несколько раз. * Research & Engineering: стабилизация процессов предобучения, проведение ML-экспериментов, проверка гипотез и доведение результатов до прода и публикаций. Требования * Уверенное владение Python и PyTorch * Distributed Training: практический опыт распределенного обучения, понимание ограничений и принципов работы под капотом (DDP, FSDP, ZeRO). * Опыт оптимизации DL-пайплайнов: профилирование и оптимизация узких инфраструктурных мест в процессе обучения (I/O bottlenecks, memory management, GPU utilization). * Экспертиза в Speech/Audio: понимание современных подходов и state-of-the-art архитектур в Speech/Audio Self-Supervised Learning. Условия * крупнейшее DS&AI community — более 600 DS-специалистов банка * дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира * возможность быть соавтором НИРов и статей для международных конференций * возможность выбрать удобный формат работы: гибрид или офис * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * ипотека выгоднее до 7% для каждого сотрудника * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Привет! Это GigaChat Vision — команда, которая делает полный цикл обучения VLM моделей, включающий в себя стадии pre-training, SFT и DPO/RL. Мы ищем сильных инженеров и исследователей, которые будут двигать вперед качество мультимодального GigaChat. _Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram. Диалог займёт примерно 10 минут. Его задача — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным!_ Обязанности * Самостоятельная работа в рамках заданного направления развития качества VLM: от планирования, до выкатки в прод; * Планирование и работа над исследовательскими задачами по архитектуре модели, стратегии и данным обучения; * Формирование гипотез по улучшению качества модели, самостоятельное планирование экспериментов по их подтверждению и постановка multi-node обучений на большом количестве GPU (тысячи A100/H100); * Решение сложных инженерных задач, развитие и оптимизация кодовой базы обучения и эвала VLM, написание чистого кода и проведение code review; * Анализ точек роста и определение способов их закрытия. Требования * Сильный технический бэкграунд в modern ML: понимание того, как данные, архитектура и стратегии обучения влияют на итоговое качество модели; * Опыт полного цикла обучения VLM или LLM; * Глубокие знания или опыт работы в области computer vision; * Понимание принципов работы распределенного обучения (FSDP) или опыт обучения моделей на больших GPU кластерах; * Наличие навыков технического лидерства: проведение code-review, помощь и менторинг младших сотрудников; * Умение работать, принимать решения и выстраивать процессы в условиях неопределенности; * Умение и желание взаимодействовать с коллегами, вносить собственные идеи и предлагать пути достижения общих целей. **Будет плюсом:** * Опыт полного цикла обучения VLM; * Наличие публикаций в области NLP или CV; * Опыт решения задач по оптимизации обучения; * Опыт участия в соревнованиях по машинному обучению или программированию. Условия * крупнейшее DS&AI community — более 600 DS-специалистов банка * дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира * возможность быть соавтором НИРов и статей для международных конференций * возможность выбрать удобный формат работы: гибрид или офис * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * ипотека выгоднее до 7% для каждого сотрудника * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.