Вакансии

Location: Paris; London ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE Mistral AI is seeking Applied Scientists and Research Engineers focused on formal methods. You will design, train, and deploy SOTA large language models and apply them to diverse use cases. You'll work cross-functionally with internal and external science, engineering, and product teams to deliver high-impact AI solutions.   WHAT YOU WILL DO - Run pre-training, post-training and deploy state of the art models on large clusters. - Generate and curate datasets for theorem proving and software verification, and build evaluators and benchmarks for formal reasoning. - Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment. - Collaborate with cross-functional teams to tackle complex use cases. - Manage research projects and communications with client research teams.   WHAT WE'RE LOOKING FOR - Fluent in English with excellent communication skills — at ease explaining complex technical concepts to both technical and non-technical audiences. - Expert with PyTorch or JAX. - Not afraid of contributing to a big codebase; finds their way around independently with little guidance. - Writes clean, readable, high-performance, fault-tolerant Python code. - Doesn’t need roadmaps: moves independently and navigates uncertainty with confidence. - Low-ego, collaborative and eager to learn. - Track record of success through personal projects, professional projects or academia. NICE TO HAVE - PhD / Master in a relevant field (Computer Science, Mathematics, Machine Learning). - Experience with autoformalization, proof automation, or verified code generation. - Experience with reinforcement learning using verifiable rewards, ideally from Lean or other proof assistants. - Contributions to a large open-source or industry codebase. - Publications in top academic journals or conferences. - Enjoys improving existing code: typing, tests, CI pipelines. 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: Paris; London ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE As a Research Engineer on the Latency & Throughput team, you will push the frontier of LLM inference by researching and prototyping techniques that reduce latency and increase throughput — from speculative decoding and quantization to KV compression and new attention variants. The team sits at the intersection of research and production: you will take ideas from recent literature, form hypotheses, run experiments, and ship the techniques that prove out into our serving stack. This role sits between our inference engineers, who own the production serving stack, and our research scientists, who focus on paper-level contributions. You will be more science-focused than a pure engineer: you are expected to generate new ideas, keep up with the state of the art, and implement proof-of-concepts that bridge the gap between academic results and production impact. Some techniques you explore will be lossy or require integration into training — retraining models, tuning architectures, or training auxiliary models to improve inference. You will work closely with the Inference Foundation and Kernels teams, who turn your validated techniques into production recipes, and with pretraining and RL teams when your work touches model training. Success is measured by demonstrated latency or throughput gains, first at small scale, then adopted in production. What You Will Do - Research and prototype inference techniques — speculative decoding, quantization, KV compression, watermarking, and new model architectures — by reading recent literature and forming testable hypotheses. - Design and run experiments to validate or refute ideas, iterating quickly on small-scale setups before proposing production integration. Demonstrate clear latency or throughput gains without unacceptable quality loss. - Collaborate with the Inference Foundation team to integrate validated techniques into the vLLM-based serving stack. - Partner with pretraining and RL teams when techniques require co-training, retraining, or training auxiliary models to improve inference. Build the right tooling and validation platforms to facilitate AI-driven inference research. What We're Looking For - A strong ML background with solid knowledge of LLM training techniques — architecture, tokenization, fine-tuning, and evaluation. - Hands-on experience with Python and PyTorch; you write clean, experimental code and iterate fast. JAX is acceptable with strong Python proficiency. C++ and Rust are not required, CUDA and GPU knowledge is expected but hands-on experience is not required. - Research experience of some form: a thesis, publications, a dedicated research project, or efficiency-oriented work in a lab or industry research setting. A PhD is not required, but we are looking for genuine research depth. - A track record of conducting experiments: forming hypotheses, designing controlled comparisons, and drawing clear conclusions from results. - Familiarity with the state of the art in LLM inference — you know what has been tried, what works, and where the open problems are. - Self-starter, low-ego, and collaborative. You are comfortable with ambiguity, negative results, and are eager to take matters into your own hands. - Inference or serving experience is a plus, not a requirement. Prior work with vLLM, production LLM deployments, or serving systems will help you ramp up faster. Location and Remote This role is based in our Paris or London offices (hybrid). We will prioritize candidates who reside in the Paris area or are open to relocating. We strongly believe in the value of in-person collaboration to foster strong relationships and seamless communication within our team. For senior profiles, we will also consider remote candidates based in France. In that case, we ask all new hires to visit our Paris office: - For the first week of their onboarding (accommodation and travel covered) - Then at least 3 days per month 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: Москва Мы ищем инженера, который будет проектировать AI-агентов для департамента ключевых клиентов. Вы будете работать в связке с заказчиком, продуктом и инженерными командами, отвечая за технический результат агента end-to-end. Обязанности * проектирование агентных архитектур: ReAct / планы-действия, tool calling, мультиагентные схемы, маршрутизация запросов между моделями и сценариями * разработка и оптимизация промптов (few-shot, CoT, structured output), управление контекстом, версионирование промптов вместе с кодом * реализация RAG: чанкинг, эмбеддинги, векторный и гибридный поиск, реранжирование, работа с метаданными и мультиязычными документами. * интеграция LLM с корпоративными системами через API и MCP-инструменты: написание и поддержка собственных tools, схем аргументов, обработка ошибок и таймаутов * построение оценочных контуров (evals): датасеты, LLM-as-a-judge, регрессионные прогоны на каждом изменении промпта или модели * защита от галлюцинаций и prompt injection, работа с отказами и обходными путями, контроль grounding на источниках * мониторинг качества и стоимости в продакшене: трейсинг, логирование шагов агента, , телеметрия токенов и задержек * упаковка агентов в сервисы: FastAPI / Python-бэкенд, контейнеризация, CI/CD, секреты, работа в контуре банка * подбор и сравнительная оценка открытых и проприетарных моделей под задачу клиента. Требования * 5+ лет в разработке ПО, из них 2+ года в практической работе с LLM и агентными системами * уверенный Python и опыт промышленной разработки: тесты, ревью, CI/CD, работа в команде * практика с фреймворками агентов и LLM-стеком: LangChain / LangGraph, LlamaIndex, OpenAI/локальные API, Pydantic, асинхронность * глубокое понимание RAG, векторных БД (Qdrant / Milvus / pgvector и др.), эмбеддингов и стратегий поиска * опыт построения evals и оценки качества LLM-систем, а не только «демо-промптов» * навыки работы с генеративными AI-моделями * опыт вывода LLM-решения в продуктив и его последующей эксплуатации * понимание ограничений моделей: контекст, стоимость, задержки, деградация качества. **Будет плюсом** ---------------- * опыт fine-tuning (LoRA/QLoRA, SFT/DPO) и работы с моделями на своих мощностях * опыт сервинга моделей (vLLM/TGI), оптимизации inference и GPU-инфраструктуры * опыт интеграций с enterprise-ландшафтом: очереди, ESB, MCP, внутренние сервисы банка * публикации, доклады, вклад в open-source, опыт наставничества. Условия * комфортный современный офис Москва, Кутузовская 32 * гибридный формат работы * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * система обучения для профессионального и карьерного развития * расширенный полис ДМС с первого дня работы и страхование для семьи * программа ипотеки для сотрудников * подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Создавать ИИ-агентов с использованием LangChain и LangGraph. Глубокое понимание основ Python: типы данных, функции и классы, обработка исключений, итераторы и генераторы, context managers, модуль typing, внутренняя структура объектов и управление памятью.

Разработка и обучение моделей машинного обучения для задач NLP и компьютерного зрения; Анализ и обработка данных;

Опыт разработки агентов на Langraph по паттерну ReAct; Опыт разработки ML-моделей от 3 лет.

Строить и проверять evals для AI-агентов: собирать тестовые наборы, считать метрики качества, искать систематические ошибки модели. Анализировать выходы агентов: распределения признаков по компаниям и отраслям, расхождения между прогонами, аномалии.

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 You'll help build the geometric foundation of the next-generation world map — joining our World Map Research Engineering team (Geospatial Alignment) as a Research Engineer or Senior Research Engineer. This is an end-to-end applied research role that spans from strong engineering and data-systems work to R&D in 3D deep learning, transformers, and foundation models. You'll develop new algorithms, produce the research data and evaluation assets needed to advance them, and engineer the resulting capabilities into dependable production systems. The World Map team works with heterogeneous captures from phones and handheld devices, aerial imagery, LiDAR, and 360 cameras, as well as external geographic and elevation data. The central challenge is to harmonize these data sources, quantify uncertainty, and maintain consistency as the map evolves. You'll work at the intersection of cutting-edge research and scalable engineering — developing new approaches to geospatial registration, correspondence, and geometric optimization, then productionizing these technologies to directly improve Niantic Spatial's world model. What You'll Do - Geospatial Registration — Develop and advance algorithms for world-scale georeferencing and registration across imagery, 3D reconstructions, point clouds, and geographic reference data. - 3D Geometry & Correspondence — Build and improve methods spanning multi-view geometry, SfM, SLAM, feature matching, learned correspondence, bundle adjustment, point-cloud registration, and ICP. - Data & Evaluation — Create the data and evaluation flywheel for world-scale mapping — design and maintain large-scale datasets, pipelines, benchmarks, and evaluation methodologies that expose hard failure modes, accelerate research iteration, and support production. - Learning-Based 3D Reasoning — Explore and develop learning-based approaches for geospatial understanding, combining transformers and 3D foundation models with geometric priors and uncertainty modeling. - Production Robustness — Design robust solutions for challenging real-world conditions: sparse or noisy GPS, changing appearance, inconsistent reconstruction quality, large geographic extents, and imperfect reference maps. - Cross-Functional Collaboration — Partner with leading researchers in visual localization, 3D computer vision, and mapping — as well as engineering and product teams — to identify high-impact problems and turn research breakthroughs into end-to-end production systems. - Research Communication — Share results clearly through technical writing, design reviews, and internal presentations; contribute to publications and open research where appropriate. WHAT YOU'LL BRING - Bachelor's or Master's degree in Computer Science, Robotics, Machine Learning, Photogrammetry, or a closely related field — or equivalent research or industry experience. - Strong grounding in 3D computer vision, machine learning, robotics, photogrammetry, or a closely related area. - Demonstrated experience with 3D geometry and several relevant areas — multi-view reconstruction, SfM, SLAM, camera pose estimation, point-cloud registration, image-based localization, georeferencing, or sensor fusion. - The ability to formulate ambiguous real-world problems, choose the right mathematical or learning-based tools, and evaluate solutions rigorously. - Strong Python skills and experience with modern ML and scientific-computing libraries (PyTorch, NumPy, SciPy). - Strong software engineering skills — you can turn research prototypes into reliable, scalable, and maintainable systems. - A track record of taking an algorithm from research idea and experimental prototype through evaluation, benchmarking, and deployment on real-world data. - Clear written and verbal communication; able to collaborate effectively across research and engineering disciplines. NICE TO HAVE - PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field — not required, but valued. - A strong publication record or active involvement in the research community at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, 3DV, ICRA, or RSS. - Experience with learning-based approaches for visual or 3D understanding — transformers, point transformers, foundation models, or methods combining learned representations with explicit geometric priors. - Experience with aerial or satellite imagery, GNSS/GPS, geodesy, mapping systems, or large-scale spatial data. - Evidence of technical leadership, mentorship, or successful cross-functional delivery. 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. We welcome applications from candidates who require visa sponsorship. 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.

— Определить, что мы считаем качеством модели — метрика, горизонт, baseline, калибровка — и довести предикт до этого уровня. — Забрать A/B-тесты пейволлов и свести их с предиктом: тест меряет краткосрочный эффект, а раскатывать надо по долгосрочной выручке.

Location: Москва, удалённая работа или гибрид, RU; Россия О команде Продавцы на Авито платят за разные целевые действия: клики, отклики, сделки, звонки. Юнит Auction Efficiency развивает модели CTR/CR, которые помогают учитывать ожидаемую выручку от платного продвижения объявлений. Команда Auction Core внедряет новые технологии в эти модели: DL на этапе ранжирования и SOTA-подходы к архитектурам. Сейчас мы уже внедрили DL для CTR в Авито Товары и продолжаем масштабировать DL под другие задачи и направления. Ищем старшего DS-инженера, который усилит команду в исследованиях и внедрении новых подходов к DL-моделям монетизации. Какие задачи вас ждут: — донастраивать существующую архитектуру эмбеддингов для задач кликов, контактов и сделок; — улучшать архитектуру обучения эмбеддингов для задач разных вертикалей; — развивать DL-подходы для задач кликов, контактов и сделок на разных поверхностях: в поиске и рекомендациях. Мы ждём, что вы: — имеете опыт работы с ML и алгоритмами; — умеете принимать решения на основании данных, наглядно аргументировать их, объяснять сложные вещи простыми словами и договариваться с коллегами; — готовы читать научные статьи и находить в них идеи, применимые на практике; — знаете и умеете применять математику, ML и прикладную статистику для создания алгоритмических систем; — видите связь между математическими формулами и их влиянием на бизнес; — готовы писать продакшен-код; — имеете опыт работы с DL и поддержкой DL-решений в продакшене; — уверенно работаете с PyTorch и имеете опыт реализации нестандартных пайплайнов обучения. Будет здорово, если вы: — занимались обучением и внедрением нейросетей на табличных данных: DCNv2, TransAct; — имеете опыт работы с ранжированием, рекомендациями, монетизацией или ценообразованием; — занимались научной работой и интересуетесь исследованиями; — участвовали и достигали результатов в соревнованиях по программированию, математике или ML. Работа у нас — это: — возможность влиять на опыт миллионов пользователей и развивать ML-продукт Авито; — значимая роль с большой зоной ответственности и возможностью определять развитие направления; — исследовательские и инженерные задачи на большом масштабе; — сильное комьюнити: команда экспертов, с которой можно обсуждать идеи и подходы; — расширенная забота о здоровье: ДМС, страхование жизни, онкострахование; — сервис психологической поддержки, а также финансовые, юридические и ЗОЖ-консультации для вас и ваших близких; — корпоративный спорт: групповые тренировки, велбиинг-приложение; — спецпредложения от партнёров и скидки на сервисы Авито: бронирование жилья, услуги продвижения, ипотечное страхование; — компенсация питания.

Location: Москва GigaChat (GigaChain): Agentic Post-Training и Online RL Обязанности **Исследовать поведение модели:** * разбирать ошибки GigaChat в длительных задачах: потерю цели и контекста, слабое планирование, неверный выбор инструментов, зацикливание, ошибки восстановления и преждевременное завершение работы * анализировать отдельные шаги и целые траектории, находить систематические паттерны успешного и неуспешного поведения * понимать источник проблемы и определять, что нужно менять: обучающие данные, objective, reward-сигнал, модель или agentic environment. **Улучшать модель:** * разрабатывать и улучшать post-training пайплайны для агентных сценариев * работать с rollout’ами, reward-сигналами, rule-based и execution-based верификаторами, judge- и reward-моделями * создавать обучающие выборки из реальных и синтетических агентных траекторий, включая фильтрацию и отбор наиболее полезных примеров * выбирать подходящий метод обучения и понимать, когда сложный RL-контур оправдан, а когда задачу эффективнее решить более простым способом. **Оценивать изменения:** * строить evaluation для многошаговых агентных задач и анализировать качество не только по финальному результату, но и по поведению модели внутри траектории * проверять, переносится ли улучшение между задачами, бенчмарками и харнесами * контролировать регрессии и воспроизводимость результатов * взаимодействовать с командами обучения, инференса, данных, API и агентных харнесов. Требования * сильное владение Python и практический опыт работы с PyTorch * практический опыт LLM post-training: SFT, preference optimization, RLHF, online RL или близкие направления * умение самостоятельно ставить ML-гипотезы, проектировать эксперименты и делать выводы по их результатам * опыт построения пайплайнов данных * навык анализа модели на уровне отдельных примеров и траекторий * способность доводить исследовательскую идею до работающего и воспроизводимого решения. Условия * 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.

• Опыт от 5 лет в Quant / ML / DS. • Сильная математическая база.

零一智能 is looking for a Knowledge Graph Algorithm Intern to build knowledge-extraction pipelines, develop agent workflows, and improve graph-based retrieval and reasoning.

Описание вакансии Мы делим продукт на стримы и сейчас собираем аналитику и ML под одного владельца. Вся мобильная реклама держится на одном разрыве: выручка от привлечённого пользователя приходит в течение года, подписками и продлениями, а решение, продолжать ли тратить на канал, нужно принять за несколько дней.

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

Сопровождение ML-решений (мониторинг сервисов и моделей, отслеживание дрейфа данных, работа над развитием); Оценка влияния на бизнес предложенных решений;

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 . At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From “How do I fix a leaky faucet?” to “What should I watch next?”, the Answers Relevance team leverages AI to connect people with the real-world wisdom of Reddit communities. We’re hiring Senior and Staff Machine Learning Engineers to make Answers smarter, more relevant, and more helpful. Our SOTA AI systems helps surface the right conversations, but the heart of Answers is still people helping people ~ Redditors sharing what they know with each other! What You’ll Work On You’ll lead ML projects from idea to production, partnering with Product, Data Science, and Engineering across recommendations, search, messaging, and AI systems. You don’t need experience in every area listed below. A background in a few may make you a great fit! Recommendations, search, content understanding, or large-scale data and ML infrastructure SOTA large scale RecSys Foundational retriever models RAG systems, retrieval methods, LLM evaluation, or production LLM features Agentic AI, such as tool calls, MCPs, multi-agent systems, or orchestration Self-hosted open-weight models such as Qwen or Llama; prompt engineering, fine-tuning, post-training, or prompt optimization Improving grounding, instruction following, and hallucination; evaluating retrieval and generation quality Scaling LLM systems, including latency, caching, and cost; safety measures for prompt injection and unsafe inputs Defining product success metrics, feedback loops, and evaluation approaches, including human review What You’ll Do Build and improve large-scale ML systems, including retrieval and ranking, embeddings, and LLM-powered features Set technical direction and deliver measurable user and business impact Work with partner teams to shape product and technical roadmaps Evaluate new AI/ML approaches and support responsible LLM development Mentor engineers and contribute to long-term ML planning Required Qualifications 4–12 years of experience building, deploying, and operating ML systems in production Broad knowledge of ML fundamentals and experience applying them in production; expert-level modeling depth isn’t required Experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers Experience writing production-quality code, including testing, evaluation, and monitoring; Python experience required, Golang experience a plus Experience with ML systems such as data pipelines, feature engineering, model training and serving, and production monitoring Strong communication and collaboration skills, and a record of delivering measurable product impact 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 #LI-Remote 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: $216,700 — $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.

Задачи: — Руководство проектированием, разработкой и внедрением on-premises MLOps решений для поддержки доставки моделей машинного обучения — Сотрудничество с дата-сайентистами и разработчиками для создания масштабируемых и эффективных конвейеров данных, систем обучения и деплоя моделей — Разработка и поддержка инструментов мониторинга и управления для обеспечения надежности и производительности MLOps инфраструктуры — Взаимодействие с заинтересованными сторонами для понимания их потребностей в машинном обучении и разработки соответствующих решений — Отслеживание новейших трендов и технологий в области MLOps, машинного обучения и искусственного интеллекта, обмен знаниями с командой — Наставничество младших членов команды и помощь в развитии их навыков в MLOps — Поиск и устранение проблем пользователей Требования: — Опыт работы 5+ лет в MLOps, разработке ПО, инженерии данных или смежных областях — Сильные навыки программирования на современных языках (Java, Scala, Python, Kotlin) — Практический опыт работы с Kubernetes: деплой и эксплуатация рабочих нагрузок, отладка сбоев, работа с основными концепциями (Deployments, ConfigMaps/Secrets, Services, resource limits) — Отличные коммуникативные и командные навыки — Ценность простоты и производительности кода Будет плюсом: — Понимание жизненного цикла ML-проектов — Опыт работы с MLOps платформами, такими как Kubeflow или MLFlow — Опыт с ML-фреймворками: scikit-learn, LGBM, Tensorflow, PyTorch — Опыт работы с Big Data инструментами: Spark, S3, Hadoop — Глубокие знания контейнеризации и оркестрации (Docker, Kubernetes) — Опыт DevOps и CI/CD практик — Опыт создания и масштабирования инструментов для обслуживания моделей, включая построение API с жесткими SLA — Страсть к инженерной стороне машинного обучения и масштабированию — Опыт проектирования и построения MLOps инфраструктуры, включая конвейеры данных, системы обучения и деплоя моделей, а также инструменты мониторинга и управления.