ML-инженер вакансии - гибрид

Коперник AI — российская R&D-продуктовая компания, которая занимается разработкой, обучением и внедрением интеллектуальных систем (AI) промышленного уровня. Задачи и требования в вакансии не указаны в тексте.

Задачи: — проводить аналитику и мониторинг работы моделей — собирать и подготавливать данные, последующий EDA, feature engineering — разрабатывать новые ML-модели — оптимизировать скрипты data/model/inference pipelines — подготавливать код проекта к проду, подготавливать, дизайнить и проводить АБ-тесты. Требования: — высшее образование по техническому направлению — от года релевантного опыта в ML — прочный математический фундамент (понимание градиентного спуска, характеристик основных распределений, PCA, МНК, базовые статистические тесты проверки гипотез) — умение и желание писать код на Python, оптимизировать его, разбираться в чужом коде, понимание SQL — владение основными библиотеками для анализа данных: pandas, numpy, scipy, matplotlib, statmodels — глубокое понимание основных алгоритмов классического ML (линейная/логистическая регрессия, деревья решений, ансамбли), базовые знания по DL (основные архитектуры, тонкости обучения) — владение основными фреймворками для ML/DL: sklearn, pytorch, transformers и др. Будет плюсом: — интерес к методам ML в задачах ценообразования и финансах — изучение Reinforcement Learning, multi-arms bandits — чтение статей по современным решениям для работы с последовательностями. Условия: — комфортный современный офис рядом с м. Кутузовская — гибридный формат работы — ежегодный пересмотр зарплаты, квартальная и годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета — программа адаптации и помощь руководителя на старте (для Junior) — расширенный ДМС, льготное страхование для семьи, корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

Требования: — Поддержка и доработка текущих приложений — Разработка новых модулей и сервисов — Обёртка моделей в API — Контейнеризация (Docker) — Мониторинг — Участие в работе с большими языковыми и визуально-языковыми моделями.

Location: New York, NY (HQ) Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT THE ROLE The Applied Science team builds models and tools that solve Ramp’s most critical problems: from underwriting businesses to combatting fraud to making spend management smarter. We’re deeply embedded in the business and provide a quantitative foundation for decision making. As an Applied Science intern, you’ll be a fully integrated member of the team and own your project from start to finish. Working with engineers, product managers, and business stakeholders, you’ll translate complex business needs into scalable machine-learning-driven solutions. This is a chance to apply ML concretely, ship code, and create genuine value for Ramp and our customers. You will focus on exciting problems in areas like: credit, fraud, growth, or our core product.   WHAT YOU’LL DO - End-to-End ML: own the model lifecycle from data exploration and feature engineering to training, benchmarking, deployment, and monitoring - State-of-the-Art AI: leverage the latest Large Language Models (LLMs) to solve novel problems and create new product capabilities for our customers - Versatile Techniques: apply the right tools to the right problems, whether it’s deep learning, gradient boosting, or causal inference - Rigorous Experimentation: quantify the impact of your work through A/B tests and other statistical methods - Collaborate: partner closely with product and business leaders to translate models and insights into actionable strategy and user-facing features   WHAT YOU NEED - B.S., M.S. or Ph.D. Student: currently pursuing a degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or other quantitative fields with an expected graduation date between Dec 2027 - 2029. Graduate degrees are preferred, but not a must. - Strong ML Fundamentals: solid understanding of the mathematical foundations of machine learning, statistics, probability, and optimization - Strong Interest or Experience with AI: curiosity and drive to integrate cutting edge LLMs and agents into applied solutions - Python Proficiency: good grasp of common Data Science libraries (pandas, scikit-learn, NumPy, PyTorch, etc.) - SQL Knowledge: experience wrangling data in a modern data warehouse (e.g. Snowflake, BigQuery, Redshift, Clickhouse) - Practical Experience: track record of curating datasets and building/evaluating ML models - Strong Communication: ability to clearly explain complex concepts to both technical and non-technical audiences and use data to build a compelling narrative - Bias For Action: a comfort with ambiguity and desire to ship solutions quickly then iterate   NICE TO HAVES - Publications, Projects, or Previous Experience: relevant experience applying AI/ML and demonstrating your passion for the field - Production ML Mindset: knowledge of software engineering best practices applied to ML including version control (Git), testing, and writing maintainable code - Data Orchestration: experience with leveraging modern data orchestration platforms (Airflow, Dagster, Prefect, Metaflow)   COMPENSATION - The monthly rate for this internship is $12,500 USD + housing stipend   RAMP BENEFITS - Apple MacBook - Catered lunches in NYC office Monday-Friday - Weekly coffee stipend BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Задачи: — Разработка ML-моделей и алгоритмов — Вывод в production ML-моделей и расчетных модулей — Аналитика на больших данных — Поддержка реализованных решений — Разработка ML-моделей и алгоритмов для прогнозирования временных рядов Требования: — продвинутые знания в области математики (мат.анализ, линейная алгебра, теория вероятностей, мат.статистика) — python: типы данных, структуры, ООП — классический ML: задачи (классификация, регрессия), метрики, basic ML stack (pandas, numpy, sklearn, catboost, xgboost), SQL — BigData stack (Hadoop, Spark, SparkML) — умение работать с git, Jira, Confluence Будет плюсом: — знания в области финансов — опыт продуктовой аналитики — опыт работы с временными рядами (анализ, разработка моделей) — опыт работы с LLM (langchain, langgraph) — опыт end-2-end разработки ML-моделей (EDA-MVP-Solution-Validation-Production) — опыт backend разработки Условия: — комфортный современный офис рядом с м. Кутузовская — гибридный формат работы — ежегодный пересмотр зарплаты, годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров — вознаграждение за рекомендацию друзей в команду Сбера.

Languages required: English (excellent communication skills), Japanese (not required, but useful).

Senior Data Engineer (ML-направление) в Ozon Банк — онлайн-банк группы Ozon, предоставляющий финансовые услуги и сервисы для покупателей и продавцов маркетплейса.

Location: Foster City, CA Workplace: Hybrid Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role Join Replit's key teams across the company, such as AI Research, Strategic Finance, or the Office of the CEO, for a unique paid internship built for sharp quantitative and creative minds. You will work alongside our top executives, in addition to world-class engineers, designers, and finance team on some of the hardest problems in AI-native software creation and accelerating key areas of our business. We are creating a dedicated track for students with strong mathematical backgrounds because the problems we are solving sit at the intersection of deep math and applied AI, including agent reasoning, systems optimization, and improving how our models learn and perform at scale. Your work will directly shape how millions of users build software. You Will: - Contribute to real engineering problems that push the boundaries of AI-powered software creation - Collaborate with engineers, designers, and product managers on infrastructure that powers Replit's platform - Prototype novel approaches to problems in AI, systems, or tooling where mathematical rigor is the differentiator - Ship work that impacts millions of developers globally, in an environment where your ideas are heard and often implemented Required Skills and Experience: - Currently pursuing a Bachelor's, Master's, or PhD in Mathematics, Computer Science, Computer Engineering, Statistics, Physics, or a related quantitative field - At least one semester of schooling remaining after the internship - Demonstrated excellence in competitive mathematics such as IMO and IOI, quantitative research, or advanced coursework - Genuine curiosity about AI, agent systems, company building, or developer tooling - Extremely bullish on Replit and the future of AI-native software creation. We would love to see what you have built on the platform What We Value: - First-principles thinking: Ability to break down complex problems from the ground up and reason about them rigorously - Mathematical intuition: Strong grasp of the underlying math behind machine learning, optimization, or systems - Self-directed and autonomous: Capable of working independently while collaborating well with cross-functional teams - Strong communication: Ability to explain complex quantitative concepts to both technical and non-technical audiences - Curiosity and speed: Genuine excitement for hard problems and the drive to iterate quickly Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder   Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

Задачи: — Проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы и автономных AI-агентов (планирование, function calling, мультиагентная оркестрация) — Дообучение и адаптация: SFT/RLHF/DPO, LoRA/QLoRA под бизнес-задачи; решать, когда нужно дообучение, а когда достаточно промпт-инжиниринга или RAG — Поддерживать культуру качества: внедрять продуктовые и ML-метрики (RAGAS, DeepEval, LLM-as-a-Judge), автоматизировать регрессионное тестирование — Писать чистый, документированный и тестируемый python-код, проектировать API-контракты для ML-компонент — Участвовать в код-ревью, формировать технические стандарты команды, работать напрямую с продуктом и стейкхолдерами Требования: — Опыт в DL/NLP от 4 лет, включая обучение и дообучение моделей на PyTorch, из них не менее года практического опыта с LLM — Практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain/LangGraph — Глубокое понимание архитектуры современных LLM: attention, MoE, RoPE, KV-cache, методы alignment (RLHF/DPO), принципы эффективного инференса — Сильная инженерная культура: Python (asyncio, pydantic, fastapi), чистый тестируемый код (pytest), асинхронные микросервисы; понимание Docker/K8s, CI/CD для ML — Оценка и мониторинг: уверенное владение инструментами LLMOps (Arize, LangSmith, W&B, Phoenix), опыт создания кастомных eval-пайплайнов — Работа с данными: опыт предобработки сложных данных для датасетов, знание SQL

Задачи: — Разрабатывать и поддерживать масштабируемые сервисы ML-моделей, оптимизируя их производительность и эффективность. — Проектировать инфраструктуру, обеспечивающую критически важные входные данные для алгоритма диспетчеризации, влияющего на опыт миллионов клиентов по всему миру. — Тесно сотрудничать с дата-сайентистами и инженерами для понимания их потребностей и создания масштабируемых решений. — Разрабатывать и улучшать инструменты ML-инжиниринга для разработки моделей, мониторинга, обслуживания и экспериментов. — Использовать облачную инфраструктуру Delivery Hero, построенную на современных технологиях и популярных облачных платформах, для создания высокодоступных систем. — Предлагать инновационные решения и новые технологии для поддержки новых кейсов. Требования: — Опыт ML-инжиниринга или MLOps не менее 4 лет, знакомство с инструментами для обслуживания моделей, обучения, экспериментов и инженерии признаков. — Опыт построения инфраструктуры для ML-пайплайнов с использованием современных инструментов и языков программирования, таких как Python. — Значительный опыт (от 3 лет) в проектировании и реализации различных моделей машинного обучения (регрессия, классификация, ансамбли деревьев) для решения бизнес-задач, с хорошей теоретической базой в статистике и теории вероятностей. — Опыт разработки приложений с нуля и создания быстрых прототипов для решения системных задач с использованием open-source инструментов. — Опыт технической оценки технологий и предложений решений на основе анализа плюсов и минусов. — Опыт глубокого анализа существующих систем для их улучшения. — Свободное владение английским языком, четкая устная и письменная коммуникация. — Желательно: опыт работы с ML-инструментами Metaflow, Airflow, MLflow, Argo Workflows, Cloud Notebooks. — Желательно: знание публичных облачных платформ (AWS, Google Cloud или аналогичных). — Желательно: опыт работы с CI/CD пайплайнами и инфраструктурой как код (Terraform или аналог). — Желательно: знание контейнеризации и оркестрации (Docker, Kubernetes, Helm). Условия: — Релокация в Берлин, Германия. — Гибридный формат работы: 2 дня в неделю в офисе на кампусе в Берлине. — 27 дней отпуска с дополнительными днями на 2-й и 3-й год работы. — Поддержка развития и карьерного роста: образовательный бюджет 1000 €, языковые курсы, поддержка родителей, доступ к Udemy Business. — Медицинские обследования, медитация, спортзал. — Программы поддержки сотрудников: план покупки акций, банк саббатикала, скидки на транспорт, страхование жизни и несчастных случаев, корпоративная пенсия. — Питание: цифровые и обычные ваучеры на еду. Компания Delivery Hero — одна из крупнейших технологических платформ Европы, работающая в 65 странах, штаб-квартира в Берлине, котируется на Франкфуртской бирже с 2017 года.

Задачи: — Разрабатывать AI-ассистентов, чат-ботов и AI-агентов. — Создавать многоэтапные AI-workflow. — Интегрировать OpenAI, Anthropic и другие LLM API. — Работать с context, memory, state и tool calling. — Разрабатывать AI-решения для Telegram, WhatsApp и Web. — Автоматизировать бизнес-процессы с помощью AI. — Разрабатывать и улучшать RAG-системы. — Работать с embeddings, semantic search, hybrid search и reranking. — Настраивать обработку, chunking и индексацию документов. — Работать с векторными, SQL- и NoSQL-базами данных. — Реализовывать цитирование и проверку источников ответов. — Разрабатывать backend на Python / FastAPI и создавать REST API. — Работать с Pydantic и асинхронным Python. — Интегрировать внешние API и базы данных. — Реализовывать retry, timeout, error handling и logging. — Контейнеризировать сервисы через Docker. — Оптимизировать промпты и системные инструкции. — Создавать тестовые сценарии и evals. — Выявлять галлюцинации и проблемы с контекстом. — Проводить регрессионное тестирование AI-функциональности. — Сравнивать модели по качеству, скорости и стоимости. — Контролировать token usage, latency, ошибки и расходы на LLM. — Анализировать реальные диалоги пользователей и улучшать AI-системы. Требования: — От 2 лет коммерческого опыта в AI Engineering, Backend, ML Engineering или смежной технической роли. — Уверенный Python. — Практический опыт работы с FastAPI. — Опыт работы с REST API, Pydantic и асинхронным Python. — Опыт работы с Docker и Git. — Практический опыт интеграции OpenAI, Anthropic или других LLM API. — Опыт работы с LangChain, LangGraph или аналогичными инструментами. — Понимание RAG, embeddings и semantic search. — Опыт разработки AI-ассистентов, чат-ботов или AI-агентов. — Опыт работы с SQL и/или NoSQL. — Понимание ограничений LLM: галлюцинации, context window, latency и стоимость. — Умение писать структурированный и поддерживаемый код. — Способность самостоятельно разбираться в задачах и предлагать технические решения. — Английский язык. Будет большим плюсом: — Qdrant, Pinecone, pgvector, Weaviate, Milvus или другие vector DB. — Production-ready RAG. — AI Agents и tool calling. — Structured Outputs, JSON Schema и Function Calling. — LLM evaluation / automated evals. — LangSmith, Langfuse, Arize Phoenix или аналогичные инструменты. — PostgreSQL, Redis, Elasticsearch / OpenSearch. — CI/CD. — AWS, GCP, Azure. — OCR и обработка документов. — Опыт разработки multi-tenant SaaS. — Понимание информационной безопасности и защиты данных. AI-инструменты в разработке: — Claude Code — Cursor — GitHub Copilot — Codex и аналоги Ожидания на первые 3 месяца: — Разобраться в существующей AI- и backend-архитектуре. — Самостоятельно развивать LLM-функциональность. — Реализовать минимум одну значимую AI-функцию от идеи до production. — Улучшить качество, стабильность, скорость или стоимость существующих AI-сценариев. — Настроить базовое тестирование и оценку качества AI-ответов. — Улучшить логирование и мониторинг ключевых AI-сценариев. Условия: — Полная или частичная занятость. — Формат: удалённо / гибрид. — График: 5/2 и другие варианты. — Рабочий день 8 часов. — Оформление по трудовому договору. — Остальные условия обсуждаются индивидуально с финальным кандидатом. — Работа над production-ready AI-системами, а не только демонстрационными решениями.

Задачи: -Планирование архитектуры приложений с использованием LLM в качестве основной технологии -Разработка ML-бэкендов и микросервисов для обеспечения функциональности и интеграции различных сервисов -Оптимизация текущих решений с акцентом на производительность и управление нагрузкой (onnx, openvino, tensorrt) Требования: -Опыт работы в роли MLE от 2-х лет -Уверенное знание Python и опыт разработки сетевых/асинхронных приложений -Широкая насмотренность в области LLM, RAG -Знание Docker, CI/CD, FastAPI и SQL Условия: -Возможна полная или частичная удалённая работа из России -Офисы в Москве, Краснодаре, Новосибирске

Location: London; Europe; Toronto Workplace: Hybrid WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role overview: North is Cohere’s AI workspace platform for enterprises: a secure, customizable environment where companies can use AI across their real workflows while maintaining control over sensitive data. North connects AI agents with workplace tools, applications, and business context, helping users delegate complex work, build automations, inspect outputs, and collaborate with AI in production environments. As North becomes more capable, one of the most important questions is also one of the hardest: how do we know whether the model is actually getting better for the workflows customers care about? This role is about being the voice of North inside modelling. You will build the evaluation systems, feedback loops, and applied modelling workflows that make sure model progress translates into better product outcomes for North users. You will work closely with North product teams, customer-facing teams, and modelling teams to define what “good” means across the product surface, turn real usage and product direction into high-quality evals, and use those evals to guide model selection, patches, and regular model updates. This is neither a pure research role nor a conventional product engineering role. It is a rigorous applied MLE role for someone who cares deeply about measurement, model behavior, and real-world product quality. You should be excited by the craft of building careful evals: evals that capture messy agentic workflows, reflect actual customer needs, resist superficial benchmark hacking, and provide useful signal for where the product and models need to go next. Please note: this team works closely across Europe and East Coast North America time zones. We are open to candidates who can collaborate effectively within those hours. Key responsibilities: - Own the eval strategy for North: define what we need to measure across agent workflows, tool use, enterprise knowledge work such as deep research, document creation or editing, and other human-AI interactions. - Build high-quality evals from the realities of the product: user feedback, production failures, privacy-preserving usage logs, internal dogfooding, customer needs, and forward-looking product goals. - Create systems that continuously turn what North is learning from users, customers, and feature teams into evals, so measurement keeps pace with the product rather than becoming a static benchmark. - Be the voice of North inside modelling: extract clear insight from eval results, customer-derived data and product context, then turn model failures, capability gaps, and North-specific needs into actionable recommendations for the central modelling teams. Qualifications: - You have improved LLM-powered, agent-powered, or AI-product systems through evals, feedback loops, data curation, prompting, model adaptation, or model selection. - You care deeply about evaluation as a craft: representative tasks, precise rubrics, clean data, failure analysis, regression tracking, and knowing when a metric is giving false confidence. - You have strong applied MLE judgment and can reason clearly about model behavior, eval validity, product outcomes, and production tradeoffs. - You are comfortable working close to users and product teams, while translating messy qualitative signals into measurement that other modelling teams can act on. - You are self-directed, practical, and motivated by open-ended problems where the right answer requires both technical depth and product understanding. - You care about making AI systems genuinely useful in real enterprise settings, not just better on abstract benchmarks. If some of the above does not line up perfectly with your experience, we still encourage you to apply. Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them. - Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled. - Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem. - Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself. - Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships. - Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve. - Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself. - Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk. REQUIREMENTS - Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack - Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems - Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems - Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product - Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership - Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it - Willingness to respond to customers outside regular working hours and participate in an on-call rotation - Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads WHAT YOU'LL BRING We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following: - Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE) - Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads - A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang - The ability to profile and optimize GPU workloads, in training or serving - Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX - Operational depth - running on-call, leading incident response, debugging distributed systems under pressure BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco Bay Area, USA Workplace: Hybrid WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands.   We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. ABOUT THE ROLE We're looking for exceptional Machine Learning Engineers focused on Ads to help take Higgsfield's advertising platform to the next level. You'll work at the intersection of large-scale machine learning, generative AI, and advertising systems—building the models and infrastructure that determine how creative is generated, ranked, optimized, and ultimately performs. This role is for someone who understands ads systems deeply and is equally strong in modern generative AI. You should be comfortable moving across ranking and recommendation, targeting and optimization, prompt engineering, post-training, and production ML systems. You'll help define what an AI-native advertising platform looks like from the ground up. WHAT YOU'LL DO: - Build and improve ML systems powering advertising products, including ranking, recommendation, targeting, prediction, and optimization. - Develop models that improve ad creative quality, relevance, personalization, and performance at scale. - Build systems that connect generative models with real-world advertising performance signals, creating feedback loops that continuously improve model outputs. - Apply prompt engineering and post-training techniques to improve generative models for advertising and creative use cases. - Work on fine-tuning, preference optimization, evaluation, and other techniques for adapting foundation models to specific creative and advertising objectives. - Design and run experiments across creative generation, ranking, targeting, and delivery to understand what drives advertiser performance. - Build production ML systems that operate reliably at significant scale, from experimentation through inference and serving. - Work closely with Product, Research, Engineering, and GTM teams to turn advances in generative AI into products advertisers can use. REQUIREMENTS - Deep experience building machine learning systems for advertising. - Strong understanding of ads systems, including areas such as ranking, recommendation, targeting, bidding, conversion prediction, creative optimization, or measurement. - Hands-on experience with LLMs, multimodal models, or generative AI systems. - Strong experience with prompt engineering and model evaluation. - Experience with post-training, including techniques such as supervised fine-tuning, preference optimization, reinforcement learning, or related approaches. - Strong software engineering fundamentals and experience shipping production ML systems. - Ability to operate across research and engineering: you can experiment quickly, identify what works, and turn it into a scalable production system. - High agency. - Working English. NICE TO HAVE - Experience building ads, ranking, or recommendation systems at a major consumer, social, search, or advertising platform. - Experience with generative video, image, or multimodal models. - Experience using downstream signals such as CTR, CVR, ROAS, engagement, or retention to train or optimize ML systems. - Experience with large-scale model training, inference optimization, or distributed ML infrastructure. - Experience building AI systems that generate or optimize advertising creative. COMPENSATION & BENEFITS • We offer a competitive and thoughtfully structured compensation package designed to align with impact, experience, and long-term growth. • Base Salary: The anticipated salary range for this role is $165 - $230k, depending on experience, skills, scope, and location. • Equity: In addition to cash compensation, employees are eligible to participate in the company’s stock option program and share in Higgsfield’s long-term growth. • • Benefits: We offer a comprehensive benefits package designed to support employees professionally and personally • The opportunity to work on ambitious AI products alongside a highly experienced, fast-moving, and international team. • Significant ownership, direct impact, and opportunities for professional growth as the company scales.   This is a hybrid role based in the San Francisco Bay Area. Team members are expected to work from our San Francisco office three full days per week, with the remaining days worked remotely and are expected to be available during agreed working hours and to maintain sufficient overlap with the relevant team’s time zone. We value in-person collaboration, active communication, responsiveness, and close partnership across teams. Higgsfield AI is an equal opportunity employer. We are committed to building a diverse and inclusive team and do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual

Location: Helsinki; Paris Workplace: Hybrid ABOUT VOODOO Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. TEAM The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile gaming industry. The Voodoo Ad-Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top-tier ad network services. Our primary goal is to leverage Voodoo's massive first-party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social network monetization following our recent acquisition of BeReal. To support this incredible trajectory and promising early results, we are scaling our team. The Models team is a core element of the targeting performance. It leverages machine learning and a strong business understanding to directly impact the product's financial performance. It's composed of mostly senior Data Analysts, Analytics Engineers, Data Engineers and Data Scientists/ML Engineers that iterate together on finding and building the next performing iteration. This role can be either Paris or Helsinki based and done in a hybrid setup. ROLE We're looking for a Senior ML Engineer to join our Models team. You will be joining a dedicated squad of Data Engineers, Data Scientists, and ML Engineers focused on building and maintaining the ML training infrastructure that powers our ad-targeting models from data preprocessing through model deployment to production monitoring. In this role, you will be leading the following topics: - Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies. - Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse). - Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints. - Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support. - ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling. - Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems. - Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers. - Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role. PROFILE We are looking for a Senior ML Engineer who deeply understands both the ML training lifecycle and the specific challenges of putting machine learning models into production at scale. - 5+ years minimum of experience as an ML Engineer or a similar role - End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration - Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs - Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping - ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning - Performance Optimization: Experience optimizing memory usage during data preprocessing and model training - Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning) - Cloud & Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics - ML Production Awareness: Understanding of the challenges involved in running ML models in production (familiar with topics such as feature store, training-serving skew, etc.) - Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus - Excellent communication skills in English OUR STACK Python · Scikit-learn · LightGBM · PyTorch · DBT · Spark · MLflow · Prometheus · Terraform · Airflow · Kubernetes · Amazon Web Services BENEFITS - Excellent benefits that will depend on the country you're based in

Location: London Workplace: Hybrid WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: Building AI agents that can assist with any kind of enterprise work is a challenging, open-ended problem. One key piece of solving it is replicating real work environments as realistically as possible - filling them with hard tasks to solve, creating plausible input data, and defining clear rewards for completing the work the right way. We build many of these reinforcement learning (RL) environments, then drop our agents into them to evaluate or train them. In this role, you are responsible for creating these RL environments, running AI agents inside them, and improving both the agents and the environments in the process. The results reach customers, whose feedback feeds back in - and the agent/environment improvement loop continues. Key Responsibilities: There are many open problems in this space. As a Member of Technical Staff, RL Environments, you will: - Build new RL environments targeting different agentic capabilities and industry areas - Train and evaluate agents in those environments - Make all the pieces work together: tasks, data, tool implementations, and verifiers - Work across modeling and product to identify gaps in agent performance, and improve both the agents and the environments - Work with external vendors to create high-quality, expert-built RL environments, and build tools to ensure high task, data, and verifier quality - Automate the discovery of model capability gaps, and systematically measure agent performance during evals and training Qualifications: You may be a good fit if: - You have engineered agents and optimized them for specific industry use cases - You have spent dozens of hours reviewing agent trajectories to pinpoint exact failure points and fix them with model training or harness engineering - You obsess over measuring agentic capabilities and turning that into a repeatable process - You have had many debates about what a good outcome from an AI agent should look like, you translated that into verifier implementations and tuned the reward designs - You have designed and run annotation workflows to surface insights into agent performance and verify data quality - You have built synthetic data pipelines to scale eval and training efforts - You use agents yourself in your daily work, and have stories about how you improved your setup to 10x your productivity - A plus: you have experience with training with RL: scaling, troubleshooting and tuning the environments If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply. Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.