Вакансии

Задачи: — Разработка и внедрение AI/ML решений с использованием LLM, Python и связанных технологий — Работа с production-grade LLM, RAG, NLP, embeddings и векторными базами данных — Парсинг документов, извлечение информации и обработка больших объемов текста — Использование LangChain, LangGraph, CrewAI, Claude Code — Работа с облачными сервисами AWS SageMaker, Bedrock, EC2, Lambda, Azure AI, OpenAI — Использование MLflow, W&B, CI/CD, Docker, Kubernetes для управления ML-процессами — Оценка AI моделей, улучшение и обеспечение наблюдаемости — Руководство командой, наставничество и коммуникация Требования: — Опыт работы в AI/ML инженерии от 3 лет — Знание Python, SQL, NumPy, Pandas, Scikit-learn, PyTorch или TensorFlow — Опыт работы с LLM, NLP, embeddings, векторными базами данных — Знание LangChain, LangGraph, CrewAI, Claude Code — Опыт работы с AWS и Azure AI сервисами — Навыки работы с MLflow, W&B, CI/CD, Docker, Kubernetes — Высшее образование в технической области Условия: — Работа в офисе, 5-дневная рабочая неделя — Локации: Hyderabad, Ahmedabad, Indore — Максимальный срок уведомления о выходе на работу: 15 дней — Процесс интервью: 3 технических/менеджерских раунда + обсуждение CTC Для отклика необходимо предоставить: — Имя кандидата, обновленное резюме, общий опыт, релевантный опыт AI/ML — Количество реализованных production AI/ML систем — Текущая компания и должность, текущее и ожидаемое вознаграждение — Местоположение и предпочтительное место работы — Наличие офферов, причина смены работы, готовность к немедленному выходу — Контактный номер, профиль LinkedIn, релевантные навыки AI/ML и LLM

Responsibilities: — Analyze data and select architectures for model training — Test new approaches and deploy models to production — Set up offline environments and integrate local LLMs Requirements: — Proficient in Python and ML tools (Pandas, Scikit-learn, PyTorch/TensorFlow) — Understanding of mathematics and statistics for algorithms — Experience with NLP, CV, or LLM architectures — Ability to package solutions in Docker — Basic Linux knowledge and API development experience — Willingness to work offline in a secure environment

Задачи: — разрабатывать и доводить до продакшена модели компьютерного зрения; — решать задачи речевой аналитики: транскрибация звонков, диаризация спикеров, извлечение сущностей и интентов, оценка качества коммуникаций и построение аналитических витрин по разговорам; — строить классический ML: скоринг, ранжирование, прогноз конверсии и оттока, рекомендательные и matching-модели на табличных данных; — проводить полный цикл ML: постановка задачи, разметка и подготовка данных, обучение, валидация, A/B и offline-оценка, мониторинг качества в проде; — упаковывать модели в сервисы (API/batch), выкатывать в продакшен, обеспечивать стабильность, latency, отказоустойчивость и версионирование; — настраивать пайплайны обучения и инференса, feature store / датасеты, трекинг экспериментов и CI/CD для ML; — работать с продуктом и бизнесом: формализовать метрики успеха, декомпозировать гипотезы, объяснять решения моделей и влияние на KPI. Требования: — уверенный Python и опыт промышленной разработки, а не только ноутбуков; — практический опыт Computer Vision (классификация, детекция, сегментация) и работы с современными CV-архитектурами; — опыт речевой аналитики / speech-to-text: ASR, диаризация, NLP поверх транскриптов; — сильный классический ML: feature engineering, градиентный бустинг, валидация, работа с дисбалансом и утечками; — опыт вывода ML в прод: сервисы инференса, batch/online, мониторинг дрифта, ретрайн, откат моделей; — понимание MLOps: Docker, оркестрация пайплайнов, трекинг экспериментов, версионирование данных и моделей; — умение читать SQL и работать с витринами/хранилищами; — плюсом — опыт GPU-инференса и оптимизации (ONNX, Triton, квантизация). Условия: — стек: Python, Java, Trino, DBT, Iceberg, Airflow, llama.cpp, LLM, Whisper, FastAPI, MLFlow, S3 + то что принесешь ты.

Задачи: — Решать сложную исследовательскую задачу на стыке probabilistic ML, sequential decision making и моделирования поведения агентов. — Моделировать поведение участников в стохастической многоагентной среде с частичной наблюдаемостью и использовать эти прогнозы для принятия решений в условиях неопределённости и ограниченного inference budget. — Проводить эксперименты и доказывать, почему новый подход лучше сильного baseline. Требования: — Опыт самостоятельной разработки или существенного улучшения ML/математических методов. — Умение работать с неопределённостью. — Опыт в одном или нескольких направлениях: RL / sequential decision making / POMDP / world models; probabilistic & Bayesian ML, latent-variable models, uncertainty estimation; behavior / opponent modeling, multi-agent systems; off-policy evaluation, importance sampling, doubly robust estimation, variance reduction; Monte Carlo / Markov models / stochastic optimization; algorithmic trading / HFT / pricing / decision systems с реальными ограничениями на latency. — Сильная математическая база, опыт собственных R&D-разработок и публикации либо сильный applied research опыт. — PhD будет плюсом, но не обязателен. — Важна способность подробно разбирать свой проект: проблема → baseline → ваша идея → альтернативы → эксперимент → измеримый результат. Условия: — Формат работы: remote. — Контракт от 3 месяцев. — Приоритетно full-time, но возможен part-time для сильных кандидатов. — Данные и вычислительная инфраструктура предоставлены, высокая исследовательская свобода. Стек для прототипирования: Python, PyTorch.

Задачи: — Развитие harness: построение эффективной обвязки вокруг модели, разработка базы знаний по вариации тест-кейсов, улучшение планера, разработка верификаторов и инструментов. — Агентское планирование в модели: обучение модели эффективным стратегиям и работе в harness для сохранения эффективности и оптимального решения задач. Требования: — Понимание построения VLM-агентов и tool calling, опыт работы с ними. — Знание RL в интерактивных средах. — Хорошее знание NLP и CV, особенно LLM и VLM. — Умение решать ML-задачи полного цикла — от сбора разметки до внедрения в продакшен. Будет плюсом: — Понимание ограничений современных VLM. — Следить за трендами в области агентов и tool-augmented-моделей. Условия: — Бесплатное участие в профильных конференциях для сотрудников. — Школа подготовки спикеров для выступающих на конференциях.

Задачи: — Развивать и поддерживать инфраструктуру DWH — Строить и развивать CI/CD-процессы для data и ML-разработки — Разрабатывать и поддерживать пайплайны обработки данных и оркестрации (Airflow) — Реализовывать end-to-end ML-пайплайны: от сбора данных до деплоя и мониторинга моделей — Проектировать и развивать MLOps-платформу (выбор и интеграция инструментов) — Разворачивать и поддерживать инфраструктуру для ML-задач: (JupyterHub + GPU, Airflow, ClickHouse, PostgreSQL, S3 и др.) — Взаимодействовать с ML- и Data-инженерами для вывода решений в production — Настраивать мониторинг и алёртинг: качество данных, стабильность пайплайнов, ресурсы — Автоматизировать рутину и развивать self-service инструменты для data-команды Требования: — Уверенный опыт с Docker и Kubernetes — Опыт работы с Airflow (или аналогами оркестрации) — Практика построения CI/CD для data/ML пайплайнов — Хорошие знания Python, SQL и Git — Опыт развёртывания ML-инфраструктуры (GPU-кластеры, хранилища, orchestration) — Опыт работы с облачными платформами — Понимание принципов CI/CD и автоматизации деплоя — Опыт работы с API и понимание highload-систем — Опыт работы со стримингом данных (Kafka / аналоги) — Практика настройки мониторинга и логирования — Уверенная работа в Linux — Будет плюсом: Опыт внедрения систем управления экспериментами (MLflow, ClearML)

О компании и команде Мы вошли в топ лучших it-работодателей России 2025 на Хабр Карьере. В Strikt мы превращаем хаотичные бизнес-процессы наших заказчиков в четкие, самообучающиеся алгоритмы с использованием ИИ. Ищем к нам в команду опытного Data Scientist для работы над интересными проектами в областях AdTech, Machine Learning! Требования: — Высшее техническое или математическое образование; — От 3–4 лет коммерческого опыта в Data Science / ML; — Сильная база в статистике, классическом ML и анализе данных; — Опыт работы с Time Series: прогнозирование, скользящее окно, тренды, сезонность, оценка качества моделей; — Опыт участия в проектах с LLM / NLP / GenAI: промпты, embeddings, RAG или интеграция LLM в продуктовые сценарии; — Уверенный Python: pandas, numpy, scikit-learn, matplotlib, Jupyter Notebook; — Знание SQL и понимание работы с реляционными БД; — Умение самостоятельно разбираться в задаче, выбирать подход и объяснять решение команде; — Готовность помогать junior/middle DS-специалистам; — Будет плюсом: Английский язык на уровне чтения технической документации PyTorch / TensorFlow / Hugging Face, CatBoost / LightGBM / XGBoost, LangChain / LlamaIndex / vector databases, понимание MLOps, мониторинга качества моделей и CI/CD. Задачи: — Работать с задачами Time Series, классического ML и LLM/NLP; — Развивать и улучшать существующие ML-модели в проектах; — Помогать выбирать подходы, метрики и критерии качества; — Анализировать данные, строить гипотезы и проводить эксперименты; — Работать с SQL и Python для извлечения, очистки и анализа данных; — Ревьюить решения менее опытных DS-специалистов и помогать команде расти. Условия: — Полностью удалённая работа; — Part-time занятость; — Оформление по договору с самозанятым или ИП с первого дня; — Проекты на стыке Data Science, Time Series, LLM/NLP и прикладного AI; — Сильная команда, возможность влиять на технические решения.

Задачи: — Участие в проекте умного ценообразования банковских продуктов — Полный цикл разработки моделей: сбор и подготовка данных, исследовательский анализ, построение uplift‑моделей, их валидация и оценка — Планирование и анализ A/B‑тестов, оценка бизнес‑эффекта — Продуктивизация: контейнеризация, интеграция в микросервисы, мониторинг качества предсказаний — Сотрудничество с бизнес‑аналитиками, продукт‑менеджерами и инженерами данных — Подготовка и проведение demo с заказчиками — Поддержка и адаптация модели к изменяющимся данным и бизнес‑условиям Требования: — Middle DS с опытом разработки полного цикла (uplift модели как преимущество) — Глубокие знания математики и статистики — Опыт работы с библиотеками и фреймворками для машинного обучения — Развитое критическое мышление — Умение формулировать гипотезы и их проверять — Навыки работы в команде и эффективное взаимодействие со смежными специалистами — Способность наглядно и понятно представлять результаты анализа, объяснять сложные вещи простым языком — Умение связывать технические решения с бизнес-задачами, учитывать специфику доменной области

Задачи: — Проводить мониторинг и обслуживание AI-моделей; — Управлять инфраструктурой в виде GPU-серверов; — Разворачивать и поддерживать современные ML-инструменты; — Документировать и стандартизировать практики вокруг обслуживания инфраструктуры для машинного обучения.

Location: Athens, Greece Are you a Senior AI/ML Lead role, looking for your next challenge? Then join our dynamic team at EUROPEAN DYNAMICS for a challenging international career. What You'll Do: Lead the design and development of ML models; Drive feature engineering, model experimentation, and evaluation; Shape the technical vision and architecture of a brand-new AI initiative; Collaborate with data engineers and business domain experts to shape the PoC and final product; Build explainable AI outputs suitable for the business domain operations; Produce early prototypes and guide technical decision-making; Lay the foundations for scaling of an AI/ML team. Requirements Mandatory University degree in IT or equivalent; 5+ years in applied machine learning (anomaly detection, fraud, risk scoring, predictive analytics); Strong Python, ML frameworks, and hands-on data skills; Experience owning end-to-end ML lifecycle (data → model → prototype); Ability to work in a small, fast-moving, exploratory environment; Excellent communication and problem-solving skills; Fluent in English (additional EU languages are a plus). Optional Experience in supply chain, customs, taxation, risk management, financial crime, or similar domains; Experience with simple UI prototyping. Benefits We believe in rewarding talent and dedication. Here's what you can expect as part of our team: Competitive full-time salary; Flexibility for a hybrid collaboration model; Private Health Coverage on the Company’s group program; Flexible Working Hours; Top-of-the-Line Tools; Work with rich, complex, high-impact datasets across security, trade, and compliance; Enjoy genuine ownership, autonomy, and the freedom to innovate; Grow with the team as we scale from PoC to production across Europe. If you want an exciting challenge, work with some of the coolest technologies, and enjoy your time doing it, then join us! Submit your detailed CV in English, quoting reference: ( SAIMLL/11/26 ). You may also consider all our other open vacancies by visiting the career section of our website ( www.eurodyn.com ) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading European Software, Information, and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Berlin, Stockholm, London, Nicosia, Valetta, Vienna, Den Haag, Hong Kong, etc.) The company employs over 1100 engineers, IT experts, and consultants (around 3% PhD, 41% MSc, and 54% BSc). We design and develop software applications using integrated, state-of-the-art technology. Our current IT projects have a value exceeding 300 million EURO. EUROPEAN DYNAMICS is a renowned supplier of IT services to European Union Institutions, international organizations, European Agencies, and national government Administrations in 40 countries and 4 continents. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, colour, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion. EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published at www.eurodyn.com/privacy . By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorize ED to process your personal data for the purposes of the company's recruitment opportunities, in line with the Policy.

Location: Vienna, Austria We currently have a vacancy for an AI/ML Engineer (Python) fluent in English, to offer his/her services as an expert who will be based in Vienna , Austria . The work will be carried out either in the company’s premises or on site at customer premises. In the context of the first assignment, the successful candidate will be integrated in the Development team of the company that will closely cooperate with a major client’s IT team on site. Your tasks Development of AI/ML models; AI solution analysis, design and implementation; Deploy machine learning models and AI solutions into production environments; Propose adequate machine learning models to extract valuable insights; Support the development of generative AI solutions; Contributing to the design of the IT architecture considering master- and meta-data management concepts; Engage with various teams, analyze business requirements and translate them into AI solutions. Requirements University degree in IT or relevant discipline, combined with minimum 7 years of relevant working experience in IT; More than 5 years of experience in the design and implementation of data processing pipelines; More than 5 years of experience in the design and implementation of AI/ML based decision support systems; More than 5 years of experience in developing software packages using Python; More than 5 years of experience in developing Deep Learning Solutions for processing image data (convolutional networks); More than 5 years of experience in major AI software frameworks like PyTorch, TensorFlow and model formats like ONNX; More than 5 years of experience in applying software engineering principles; Experience with developing Large Language Models or multimodal models processing image data; Experience with building JSON REST, including building and deployment of service-oriented architecture components; Experience with ClearML or similar platform for streamlining AI development and deployment; Knowledge of models for real-time object detection and image segmentation (e.g., YOLO); Familiarity with Windows and Linux operating systems; Familiarity with Git source control; Excellent command of the English language. Benefits If you are seeking a career in an exciting and dynamic company, where you will offer your services as part of a team of a major European Institution, operating in an international, multilingual and multicultural environment where you can expect real chances to make a difference, please send us your detailed CV in English, quoting reference: (18825/11/26). We offer a competitive remuneration (either on contract basis or remuneration with full benefits package), based on qualifications and experience. All applications will be treated as confidential. You may also consider all our other open vacancies by visiting the career section of our web site ( www.eurodyn.com) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS ( www.eurodyn.com ) is a leading Software, Information and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Stockholm, London, Nicosia, Hong-Kong, Valetta, etc). The company employs over 1000 engineers, IT experts and consultants (around 3% PhD, 36% MSc and 53% BSc or equivalent). We design and develop software applications using state-of-the-art technology. The group generates annual revenues in the range of EURO 40 million, with an EBITDA in the range of 20%. The value of our contract portfolio exceeds EURO 250 million. EUROPEAN DYNAMICS is a renowned supplier of IT services to government institutions, multinational corporations, public administrations and multinational companies, research and academic institutes. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, color, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion. EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published in www.eurodyn.com/privacy . By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorise ED to process your personal data for the purposes of the company's recruitment opportunities, in line to the Policy. Furthermore, when providing your data, it is up to you to explicitly consent that your data can be assessed for future job openings, for as long as you do not withdraw such consent. If you do not consent, we will not be able to consider the data you provide to us for future job openings.

Location: Athens, Greece Are you a passionate IT Consultant with expertise in Artificial Intelligence (AI) and Machine Learning (ML)? We’re seeking a dynamic professional to join our team, either at our Athens offices or remotely. This role provides an exciting opportunity to work on challenging IT software projects for major international public organizations alongside a highly skilled team What You'll Do: Participate in requirements elicitation meetings and contribute to brainstorming sessions; Elaborate functional/non-functional specifications; Analyze data originating from various data sources, in order to drive optimization and improvement of product development and business strategies; Identify candidate models and algorithms matching the business problems; Develop PoC and enterprise solutions; Contribute to the full development lifecycle, through requirements gathering, analysis, design, implementation, testing, documentation, and maintenance of system components; Operate in a multinational environment, adhering to highly professional standards and methods; Ensure the delivery of quality products. Requirements University degree in Data Science, Computer Science, Information Technology, or other related fields; Good understanding of a variety of AI and machine learning techniques and their real-world advantages/drawbacks; Good understanding of data modeling and evaluation; Good understanding of the ML lifecycle; Experience with Exploratory Data Analysis (EDA); Strong analytical capabilities, team and quality-oriented, keen to learn and excel; Excellent written and oral communication skills in English. Nice to Have: Experience with Big Data technologies, such as Spark (and PySpark); Experience with SQL and exposure to ETL development. Benefits We believe in rewarding talent and dedication. Here's what you can expect as part of our team: Competitive full-time salary; Private Health Coverage on the Company’s group program; Flexible Working Hours; Top-of-the-Line Tools; Professional Development: Benefit from language courses, specialized training, and continuous learning opportunities; Career Growth: Work with some of the most innovative and exciting specialists in the industry; Dynamic Work Environment: Thrive in a setting that offers challenging goals, autonomy, and mentoring, fostering both personal and company growth. If you want an exciting challenge, work with some of the coolest technologies, and enjoy your time doing it, then join us! Submit your detailed CV in English, quoting reference: ( AIML/11/26 ). You may also consider all our other open vacancies by visiting the career section of our website ( www.eurodyn.com ) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading European Software, Information, and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Berlin, Stockholm, London, Nicosia, Valetta, Vienna, Den Haag, Hong Kong, etc.) The company employs over 1100 engineers, IT experts, and consultants (around 3% PhD, 41% MSc, and 54% BSc). We design and develop software applications using integrated, state-of-the-art technology. Our current IT projects have a value exceeding 300 million EURO. EUROPEAN DYNAMICS is a renowned supplier of IT services to European Union Institutions, international organizations, European Agencies, and national government Administrations in 40 countries and 4 continents. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, colour, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion. EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published at www.eurodyn.com/privacy . By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorize ED to process your personal data for the purposes of the company's recruitment opportunities, in line with the Policy.

Задачи: — Преобразовывать бизнес-требования в ML-задачи, собирать пайплайн, обучать и проверять модель, внедрять её в реалтайм-сервис и тестировать с помощью A/B-теста. — Искать точки роста, предлагать и отстаивать свои идеи, запускать рекомендации на новых поверхностях и вертикалях. — Ранжировать с учётом предпочтений, доступности и загрузки ресторана, скорости и стоимости доставки, а также балансировать органику с рекламным продвижением. — Сотрудничать с продуктовыми командами, аналитиками и MLOps, решать общие задачи, проводить совместные эксперименты и защищать результаты. Требования: — Опыт разработки на Python или C++. — Хорошее знание методов анализа данных и ML с практическим применением. Будет плюсом: — Опыт создания рекомендательных систем или поиска. — Опыт работы с высоконагруженными реалтайм-системами.

Location: Amsterdam, Netherlands; Berlin, Germany; London, United Kingdom; Poland; Prague, Czech Republic; Tel Aviv, Israel; Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Задачи: — Участие в операционной деятельности команды: дейли-митинги, спринт-планирования, ретроспективы, проведение демо для заказчика; — Контроль соблюдения сроков и качества результатов по всем активным проектам; — Проектирование и внедрение LLM решений: NER/классификация/обогащение, semantic search и RAG пайплайны, подготовка датасетов, fine tuning, улучшение метрик; — Ведение проектной документации в соответствии с принятыми стандартами и регламентами компании. Требования: — Опыт DS/ML (3+ лет) и общий опыт в IT около 6 лет, с фокусом на end-to-end внедрения AI/ML решений; — Опыт оценки трудозатрат для комплексных AI-проектов; — Инструменты управления: Jira, Confluence или аналоги для трекинга задач, ведения документации и управления бэклогом; — Понимание полного жизненного цикла ML-проекта: сбор данных, разметка, обучение, валидация, деплой, мониторинг; — Понимание принципов работы LLM, GenAI, RAG и агентных систем — на уровне, достаточном для оценки рисков; — Понимание принципов MLOps: CI/CD для моделей, версионирование данных, автоматизация пайплайнов; — Высокий уровень владения языком Python и опыт разработки production решений и интеграций с корпоративными системами; — Практика работы с корпоративными базами данных (PostgreSQL, ClickHouse, Vertica или аналогичными) и построением витрин/ETL (Airflow, Kafka и т.п.). Условия: — работа в аккредитованной IT-компании (льготная ипотека и проч.); — оформление по ТК РФ и его полное соблюдение (оплачиваемые отпуска, больничные); — ДМС со стоматологией с первых недель работы (а не после испытательного срока).

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale. This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet. Key responsibilities Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale Own end-to-end performance of our largest RL runs, from research code down to the hardware Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause Minimum qualifications Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack Preferred qualifications Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise Experience developing RL algorithms for language models Experience with scaling laws or other quantitative models of training efficiency Experience designing or modifying transformer architectures beyond standard configurations Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices Experience with C++ or Rust Representative projects Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Requirements: — 4–5+ years of AI/ML or Data Science experience — Advanced Python, SQL, and ML experience — Hands-on experience with LLMs, RAG, embeddings, and transformers — Experience with PyTorch/TensorFlow, Hugging Face, LangChain or LlamaIndex — Experience with vector databases such as Pinecone, FAISS, Milvus, or OpenSearch — MLOps experience with Docker, Kubernetes, MLflow, and CI/CD — AWS and production AI/ML deployment experience The role involves building production GenAI systems, RAG pipelines, recommendation/personalization models, and scalable ML infrastructure from development through deployment. The position is fully remote, with full-time or contract options.

Задачи: — Отвечать за всю AI-часть продукта: архитектура AI-системы от выбора LLM-моделей до построения RAG-pipeline и интеграции компьютерного зрения, принимать все ключевые технические решения — Обеспечивать качество рекомендаций, повышая конверсию на каждой итерации — Проводить быстрые итерации с выкатыванием изменений каждую неделю, сбором метрик, анализом и улучшением — Проводить эксперименты: A/B тесты промптов, гибридный поиск (текст + визуал + отзывы), мультимодальные модели, быстро проверять гипотезы на реальных пользователях — Масштабировать систему с 2000 туров на Мальдивы до 50,000+ туров по трём направлениям с тысячами запросов в день — Строить продукт с нуля, быстро экспериментировать и влиять на архитектуру Требования: — Опыт с продакшен LLM-приложениями, построение RAG-систем или чат-ботов на основе GPT/Claude — Экспертный уровень промпт-инжиниринга: правильная структура промптов, few-shot examples, chain-of-thought — Опыт работы с эмбеддингами и векторным поиском, понимание similarity search, hybrid search, reranking — Python на уровне продакшена: чистый, быстрый, поддерживаемый код Будет плюсом: — Опыт с компьютерным зрением и мультимодальными моделями (CLIP, GPT-4o, Claude 4.5 Sonnet) — Опыт с high-load системами и оптимизацией latency — Понимание рекомендательных систем — Понимание MLOps: мониторинг моделей, версионирование, A/B тестирование — Опыт построения recommendation систем и MCP для LLM систем — Личный опыт использования LLM для автоматизации задач Софт-скиллы: — Продуктовое мышление: AI как инструмент решения бизнес-задач — Умение работать с метриками: конверсия, время сессии, escalation rate — Быстрота принятия решений: выкатывать несовершенное решение и тестировать — Умение объяснять сложное простым языком для команды и бизнеса Условия: — Официальное оформление по ТК РФ — График 5/2, офис, сокращённый рабочий день по пятницам до 15:30 — Корпоративные программы обучения и развития — Медицинское обслуживание: ДМС и собственная медсанчасть — Специальные предложения от компаний-партнеров (рестораны, магазины, отдых, мероприятия) — Организация отдыха сотрудников и их детей, оплата детского сада — Аккредитованная IT компания — Офис на станции м. Курская — Корпоративные условия на приобретение туров — Дополнительный оплачиваемый отпуск 3 дня, оплата больничного — ДМС для членов семьи по корпоративным условиям — Корпоративные условия на изучение английского и занятия спортом