Вакансии

Location: Москва Workplace: Гибрид, Удаленно RWB (Wildberries & Russ) — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Мы ищем Middle+ / Senior ML Engineer в команду Поиска для развития направления Спеллчека пользовательских запросов и карточек товаров. При этом мы занимаемся широким спектром задач, связанных с обработкой текста в движке Поиска. Обязанности Улучшать текущие и развивать новые пайплайны обработки текста в запросах и карточках товаров (исправление опечаток, синонимизация); Развивать направление онлайн-инференса в продуктах обработки текста; Разрабатывать пайплайн автоматического выделения текстовых синонимов для применения в ретривинге и ранжировании; Разрабатывать модель классификации запросов (текстовые, категорийно-атрибутивные, брендовые, нетекстовые); Исследовать методы кластеризации (IVF и др.) для ускорения и повышения качества векторного поиска. Требования Опыт работы в роли ML / NLP Engineer от 3 лет; Уверенное владение Python и стандартным ML/DS-стеком (polars, pytorch и т.д.); Опыт работы со Spark и Airflow (построение и поддержка пайплайнов обработки данных, ETL); Опыт разработки и внедрения трансформерных моделей (обучение, файнтюнинг, инференс — HuggingFace Transformers и т.п.); Опыт работы с табличными моделями (LightGBM, CatBoost, XGBoost) — построение ранжирующих/классификационных моделей; Опыт применения методов кластеризации (например, IVF и другие подходы для векторных индексов/ANN-поиска); Понимание задач NLP: нормализация текста, работа с опечатками, синонимия, классификация текстов; Опыт вывода моделей в продакшн, понимание требований к онлайн-инференсу (латентность, нагрузка); Умение работать с большими объёмами данных, оптимизировать пайплайны обработки; Опыт работы с поисковыми системами (retrieval, ranking, семантический поиск) и векторными базами данных / библиотеками ANN-поиска (FAISS, HNSW и т.п.) будет плюсом. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

Location: Москва Workplace: Гибрид, Офис Объединённая компания Wildberries и Russ — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. О команде и проектах: Full-stack ML/AI команда с продуктовым мышлением. Внедряем ML/AI в бизнес Финтех/Банка в РФ и странах СНГ. Ищем сильного ML Lead, готового руководить ML командой в B2С. Обязанности Руководить командой ML-инженеров; Участвовать в найме и развитии команды; Взаимодействовать со стейкхолдерами продукта и смежными командами; Строить и развивать внутренние пайплайны для разработки ML моделей; Разрабатывать и выводить в production ML-модели: скоринговые, оттока, отклика, прогноза продаж; Решать задачи Risk-моделирования (PD/LGD/PnL), CRM, Collection; Исследовать и интегрировать новые источники данных для расширения признакового пространства; Выстраивать мониторинг за стабильностью моделей и фичей; Проводить код-ревью и задавать стандарты качества кода. Требования Опыт и бэкграунд: Опыт в DS/ML от 5 лет, из них минимум 1 год в банке, финтехе или на маркетплейсе; Промышленный опыт внедрения моделей в production; Опыт работы с S3/Hadoop и аналитическими БД (ClickHouse, Trino, GreenPlum); Опыт руководства командой или проектами от 1 года; Опыт найма сотрудников и построения команды; Умение декомпозировать задачи и планировать работу команды. ML и DL: Глубокое владение классическим ML и DL: градиентный бустинг, uplift- и behavioral-модели, нейросети и трансформеры; Опыт построения мониторинга за работой моделей; Опыт работы с LLM/агентами для решения повседневных задач Технологии и инструменты: Владение Python, Spark, PyTorch, Airflow, Git, Docker, SQL; Опыт работы с инструментами командной работы (Git, Jira, Confluence). Будет плюсом: опыт работы с MLflow/ClearML, Triton inference, графовой аналитикой/графовыми NN, RL. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

Location: Москва Workplace: Гибрид Объединённая компания Wildberries и Russ — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Мы Full-stack ML/AI команда с продуктовым мышлением. Внедряем ML/AI в бизнес Финтех/Банка в РФ и странах СНГ. Ищем сильного ML Lead, готового руководить ML командой в B2С. Обязанности Руководить командой ML-инженеров; Участвовать в найме и развитии команды; Взаимодействовать со стейкхолдерами продукта и смежными командами; Строить и развивать внутренние пайплайны для разработки ML моделей; Разрабатывать и выводить в production ML-модели: скоринговые, оттока, отклика, прогноза продаж; Решать задачи Risk-моделирования (PD/LGD/PnL), CRM, Collection; Исследовать и интегрировать новые источники данных для расширения признакового пространства; Выстраивать мониторинг за стабильностью моделей и фичей; Проводить код-ревью и задавать стандарты качества кода. Требования Опыт в DS/ML от 5 лет, из них минимум 1 год в банке, финтехе или на маркетплейсе; Промышленный опыт внедрения моделей в production; Опыт работы с S3/Hadoop и аналитическими БД (ClickHouse, Trino, GreenPlum); Опыт руководства командой или проектами от 1 года; Опыт найма сотрудников и построения команды; Умение декомпозировать задачи и планировать работу команды. Глубокое владение классическим ML и DL: градиентный бустинг, uplift- и behavioral-модели, нейросети и трансформеры; Опыт построения мониторинга за работой моделей; Опыт работы с LLM/агентами для решения повседневных задач. Владение Python, Spark, PyTorch, Airflow, Git, Docker, SQL; Опыт работы с инструментами командной работы (Git, Jira, Confluence). Будет плюсом: опыт работы с MLflow/ClearML, Triton inference, графовой аналитикой/графовыми NN, RL. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

Workplace: Удаленно Объединённая компания Wildberries и Russ — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Сейчас мы разрабатываем несколько ML-продуктов в системе управления складом (WMS Коледино) и ищем Data Scientist с опытом разработки полного цикла ML-решений (Classic ML + Computer Vision) и уверенным знанием алгоритмов и структур данных. ML-стек: PyTorch, Pandas, NumPy, Scikit-learn, CatBoost/XGBoost/LightGBM, Hadoop, Pyspark, PostgreSQL, ClickHouse. Обязанности Разрабатывать архитектуру ML-пайплайна на основе бизнес-требований; Обучать модели машинного обучения (Classic ML + Computer Vision); Улучшать качество текущих моделей и бизнес-метрик решений; Проводить анализ результатов, метрик и формулировать гипотезы по улучшению моделей; Выкатывать модели в Production и проводить A/B тесты; Требования Опыт разработки полного цикла ML-решений; Уверенный опыт обучения и использования современных моделей компьютерного зрения; Опыт в Data science от 3-х лет; Уверенные знания алгоритмов и структур данных, прикладной статистики и мат. анализа; Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

Location: AU: Sydney (45 Clarence St); AU: Melbourne: (260 Burwood Rd) Workplace: Hybrid The role You will design, build and own the production systems behind Xero’s AI features: the services, pipelines and infrastructure that power LLM-driven and agentic experiences for millions of small businesses every day. This is a software engineering role. You’ll build and operate systems rather than train or tune models, and your architecture decisions will directly shape scale, cost, reliability and how fast our teams ship. You don’t need a machine learning background. We’re looking for strong production engineers, ideally with experience building LLM or agentic systems, and ML depth can be learned on the team. Beyond architecture, you’ll lift engineering capability across AI Products by setting standards, mentoring engineers and partnering across Xero to make data usable and systems dependable.   The team You’ll join AI Products, a diverse, globally distributed team of scientists, engineers, product managers and analysts within our broader Data & AI business. Engineers here partner closely with Applied Scientists early on to build the interfaces and harnesses that take models into production. They then own the engineering and infrastructure of every ML and AI system shipped into production, from reliability and cost to scale. Together, this collaborative team reduces toil and delivers reliable, intelligent experiences for small businesses. The team is currently working on - Production ML and AI engineering: Building and operating the platforms, services and practices that support traditional ML models, generative AI and agentic systems in production. - JAX (Just Ask Xero): Powering Xero's conversational GenAI assistant and evolving the underlying capabilities to support increasingly sophisticated, context-aware and agentic experiences. - Intelligent financial workflows: Developing capabilities such as Automated Bank Reconciliation and smart document capture, using ML and AI to match transactions, extract information from bills and receipts, and automate complex tasks. - XeroForce: Building the platform for AI-powered workflows, enabling agents to plan and execute complex tasks across Xero’s products and supporting reliable, reusable agentic experiences at scale. - End-to-end AI product experiences: Integrating AI into customer-facing experiences and product workflows, alongside the backend services, orchestration and infrastructure that make them work.   Where and how you can work Xero offers a flexible hybrid working model designed to blend the collaboration of office life with the autonomy of remote work. You will have access to our modern office spaces, with expectations around office days and collaborative 'boost days' aligned to help your team connect and ship great code effectively. Here are some of the things we are looking for - A track record of designing, shipping and operating production-grade backend systems at scale - Experience building and operating production systems for LLM or agentic workloads (orchestration, tool use, evaluation, guardrails, latency and cost management) - Strong system design fundamentals and excellent coding in Python, or another language suited to web-scale services (Go, Java, Kotlin, TypeScript) - Experience with APIs and service design, cloud infrastructure (ideally AWS), CI/CD and observability, with a focus on latency, cost, reliability and SLOs - Experience running services in production, including on-call and incident response - Solid SQL and experience with large-scale data processing - Excellent communication, with the ability to explain complex technical concepts to business and technical audiences - A coaching mindset and the ability to set engineering standards and mentor other engineers Nice to have - Experience operationalizing ML or LLM workloads (inference serving, evaluation pipelines) - Familiarity with MLflow, PyTorch or TensorFlow, and data orchestration tools - Distributed processing experience (Spark, Dask)   Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Location: AU: Sydney (45 Clarence St); AU: Melbourne: (260 Burwood Rd) Workplace: Hybrid The role You will design, build and own the production systems behind Xero’s AI features: the services, pipelines and infrastructure that power LLM-driven and agentic experiences for millions of small businesses every day. This is a software engineering role. You’ll build and operate systems rather than train or tune models, and your architecture decisions will directly shape scale, cost, reliability and how fast our teams ship. You don’t need a machine learning background. We’re looking for strong production engineers, ideally with experience building LLM or agentic systems, and ML depth can be learned on the team. Beyond architecture, you’ll lift engineering capability across AI Products by setting standards, mentoring engineers and partnering across Xero to make data usable and systems dependable.   The team You’ll join AI Products, a diverse, globally distributed team of scientists, engineers, product managers and analysts within our broader Data & AI business. Engineers here partner closely with Applied Scientists early on to build the interfaces and harnesses that take models into production. They then own the engineering and infrastructure of every ML and AI system shipped into production, from reliability and cost to scale. Together, this collaborative team reduces toil and delivers reliable, intelligent experiences for small businesses. The team is currently working on - Production ML and AI engineering: Building and operating the platforms, services and practices that support traditional ML models, generative AI and agentic systems in production. - JAX (Just Ask Xero): Powering Xero's conversational GenAI assistant and evolving the underlying capabilities to support increasingly sophisticated, context-aware and agentic experiences. - Intelligent financial workflows: Developing capabilities such as Automated Bank Reconciliation and smart document capture, using ML and AI to match transactions, extract information from bills and receipts, and automate complex tasks. - XeroForce: Building the platform for AI-powered workflows, enabling agents to plan and execute complex tasks across Xero’s products and supporting reliable, reusable agentic experiences at scale. - End-to-end AI product experiences: Integrating AI into customer-facing experiences and product workflows, alongside the backend services, orchestration and infrastructure that make them work.   Where and how you can work Xero offers a flexible hybrid working model designed to blend the collaboration of office life with the autonomy of remote work. You will have access to our modern office spaces, with expectations around office days and collaborative 'boost days' aligned to help your team connect and ship great code effectively. Here are some of the things we are looking for - A track record of designing, shipping and operating production-grade backend systems at scale - Experience building and operating production systems for LLM or agentic workloads (orchestration, tool use, evaluation, guardrails, latency and cost management) - Strong system design fundamentals and excellent coding in Python, or another language suited to web-scale services (Go, Java, Kotlin, TypeScript) - Experience with APIs and service design, cloud infrastructure (ideally AWS), CI/CD and observability, with a focus on latency, cost, reliability and SLOs - Experience running services in production, including on-call and incident response - Solid SQL and experience with large-scale data processing - Excellent communication, with the ability to explain complex technical concepts to business and technical audiences - A coaching mindset and the ability to set engineering standards and mentor other engineers Nice to have - Experience operationalizing ML or LLM workloads (inference serving, evaluation pipelines) - Familiarity with MLflow, PyTorch or TensorFlow, and data orchestration tools - Distributed processing experience (Spark, Dask)   Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Location: São Paulo, Brazil; Buenos Aires, Argentina; Bogotá, Colombia; Lima, Peru New opportunities in Latin America! 🟢 Dev.Pro is a US-based software development company helping tech companies build their products and grow their business. We’re remote-first, and we work with independent contractors in over 55 countries, so you’d be collaborating in a multicultural environment on some really interesting projects. We invite a skilled AI Engineer to join our team in building GPU-accelerated video analytics for real-time safety monitoring across large fleets and industrial environments. In this role, you’ll help design and optimize pipelines that detect and track actionable safety events in high-volume video streams. You’ll work on real-world systems that make an impact, collaborating with engineering and product teams to deliver reliable, high-performance solutions. 🟩 What's in it for you: Collaborate with top engineers on an innovative, technically challenging computer vision project Build impactful systems that enhance safety across large fleets and industrial operations Gain hands-on experience with GPU-accelerated video analytics, temporal tracking, and real-time safety monitoring ✅ Is that you? 7+ years of software engineering experience with strong backend skills 3+ years of experience shipping computer vision solutions to production Strong Python proficiency Experience with OpenCV, PyTorch, async I/O frameworks, and API integrations Hands-on experience with YOLO/Ultralytics or similar object detection frameworks Experience detecting, labeling, and tracking objects in video Solid knowledge of frame sampling, confidence thresholds, temporal filtering, and multi-camera aggregation Experience with GPU inference optimization (batching, stride, TensorRT, CUDA, quantization, throughput tuning) Ability to communicate and collaborate effectively with engineering and product teams Upper-Intermediate+ English level Desirable: Experience with Azure Event Hub, Blob Storage, Application Insights, or similar Familiarity with Docker, cloud deployments, and production monitoring systems Experience in temporal/sequence analysis for event detection Experience with video analytics in safety, compliance, or industrial/transportation environments 🧩 Key responsibilities and your contribution In this role, you’ll develop and maintain robust backend solutions that process video data and deliver actionable insights using pre-trained computer vision models. Develop and optimize GPU-accelerated video inference pipelines for real-time processing Implement, evaluate, and improve object detection models (YOLO or similar) Build temporal smoothing and tracking logic for safety event detection Optimize model performance using TensorRT, ONNX, CUDA, and GPU profiling tools Build and maintain integrations with event-driven APIs, Azure Event Hub, Blob Storage, and internal services Add metrics, logging, telemetry, and fail-safe mechanisms for resilient inference jobs Contribute to dataset curation, labeling, and model evaluation; fine-tuning/training as needed Support containerized deployments (Docker) and assist with monitoring and scaling production workload Our next steps: ✅ Submit a CV in English — ✅ Intro call with a Recruiter — ✅ Interview — ✅ Signing the Contract Interested? Find out more: 📋 How to Join Us 💻 LinkedIn Page 📈 Our website 💻 IG Page By submitting application, I've read and agree to the Dev.Pro Private Policy and Consent on Processing of Personal Data . I agree to have my Personal Data being stored and processed by Dev.Pro.

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Velaura is looking for an Edge AI SDK Lead to lead customer-facing embedded SDK development, developer tools, documentation, and release quality.

Ищем кванта, который сам закрывает полный цикл ресерча. Кого ищем 🔍 Senior Quant/ML Researcher, который закрывает полный цикл ресерча сам: от гипотезы и данных до бэктеста, валидации и результата.

Ищем кванта, который сам закрывает полный цикл ресерча. Кого ищем 🔍 Senior Quant/ML Researcher, который закрывает полный цикл ресерча сам: от гипотезы и данных до бэктеста, валидации и результата.

Ищем кванта, который сам закрывает полный цикл ресерча. Кого ищем 🔍 Senior Quant/ML Researcher, который закрывает полный цикл ресерча сам: от гипотезы и данных до бэктеста, валидации и результата.

Location: Москва «История операций» — это один из самых высоконагруженных и посещаемых экранов в приложении. Наша цель — превратить сухие строчки чеков в умную, интерактивную ленту. Мы внедряем LLM, чтобы пользователь мог общаться со своими финансами на естественном языке, получать моментальные умные инсайты, автоматические теги и персонализированную аналитику расходов. Обязанности * Разрабатывать и внедрять ИИ-агентов (LLM Agents): проектировать логику рассуждений агентов на базе GigaChat, обучать и дообучать модели (Finetuning, LoRA/QLoRA), настраивать вызов внешних инструментов (Tool Calling, API, SQL-скрипты) под задачи банкинга. * Проектировать и обучать классические ML-модели: разработать точечную ML-модель для анализа транзакционных и табличных данных (классификация, кластеризация или скоринг) для обогащения контекста агента. * Строить RAG-системы: проектировать архитектуру контекстного поиска ответов по сложным финансовым логам, чекам и документам пользователя. * Анализировать и дорабатывать корзину запросов: оценивать результаты ответов агентов на предмет качества и ошибок, согласовывать метрики (Guardrails, Ragas), защищать подходы перед бизнесом и показывать ценность данных на понятном языке. * Обеспечивать надёжность и оптимизировать воркфлоу: минимизировать задержки (latency) ответов, настраивать мониторинг внедрённых решений и контролировать траекторию поведения агента, полностью исключая галлюцинации в продакшене. Требования Наш идеальный кандидат * Имеет опыт в LLM от 2-3 лет: запускал языковые модели в продакшн (Transformers, BERT, GPT-подобные архитектуры). * Мастерски владеет Python: знает библиотеки PyTorch, Hugging Face, LangChain, LlamaIndex. * Понимает специфику работы с LLM: умеет эффективно настраивать Prompt Engineering, оптимизировать инференс (vLLM, TensorRT-LLM) и оценивать качество генерации (Ragas, TruLens). * Обладает базой в классическом машинном обучении: понимает алгоритмы Classic ML, умеет работать с табличными данными и признаками (CatBoost / LightGBM / Scikit-learn). * Дружит с Big Data: уверенно пишет сложные запросы на SQL (Hive/Spark) для подготовки датасетов. Условия Почему это вакансия-мечта * Масштаб и влияние: ваш код и обученные модели будут работать в смартфонах каждого второго жителя страны. * Топовый стек и ресурсы: доступ к мощным суперкомпьютерам Сбера для обучения крупнейших моделей. * Минимум бюрократии: мы работаем по гибким методологиям внутри сильного DS-сообщества. * Забота и бенефиты: конкурентная белая зарплата, годовые бонусы, ДМС * Тренажерный зал (работает даже сейчас) * Современный IT-офис с пуфами, игровыми зонами и т.д. * Умопомрачительные виды на Москву Профильное обучение, митапы, конфы, воркшопы внутри и вне компании Если вы хотите быть у истоков создания принципиально новой, умной истории операций — давайте создавать будущее финтеха вместе!

biz middle darajadagi backend (python)/ai engineer izlayapmiz.

Location: Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY 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 Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development Independently lead small research projects while collaborating with team members on larger initiatives Design, run, and analyze scientific experiments to advance our understanding of large language models Optimize and scale our training infrastructure to improve efficiency and reliability Develop and improve dev tooling to enhance team productivity Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field Strong software engineering skills with a proven track record of building complex systems Expertise in Python and experience with deep learning frameworks (PyTorch preferred) Familiarity with large-scale machine learning, particularly in the context of language models Ability to balance research goals with practical engineering constraints Strong problem-solving skills and a results-oriented mindset Excellent communication skills and ability to work in a collaborative environment Care about the societal impacts of your work Preferred Experience Work on high-performance, large-scale ML systems Familiarity with GPUs, Kubernetes, and OS internals Experience with language modeling using transformer architectures Knowledge of reinforcement learning techniques Background in large-scale ETL processes You'll thrive in this role if you Have significant software engineering experience Are results-oriented with a bias towards flexibility and impact Willingly take on tasks outside your job description to support the team Enjoy pair programming and collaborative work Are eager to learn more about machine learning research Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term. Sample Projects Optimizing the throughput of novel attention mechanisms Comparing compute efficiency of different Transformer variants Preparing large-scale datasets for efficient model consumption Scaling distributed training jobs to thousands of GPUs Designing fault tolerance strategies for our training infrastructure Creating interactive visualizations of model internals, such as attention patterns 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.