ML-инженер вакансии - удалённо

Задачи: - Интегрировать LLM в продукты и сервисы (API, backend, пайплайны обработки данных); - Разрабатывать и оптимизировать prompt’ы, CoT, системные инструкции; - Оценивать качество ответов (метрики + human eval); - Настраивать guardrails: безопасность, фильтрация, соответствие требованиям; - Строить RAG-системы: индексация данных; работа с векторными БД; оптимизация поиска и генерации; - Оптимизировать инференс (стоимость, latency, batching, caching, квантизация); - Участвовать в архитектурных решениях вместе с продуктовой и ML-командой; - Исследовать и внедрять новые подходы и модели; - При необходимости: работать с мультимодальными моделями (текст, изображения, документы). Требования: - Уверенный Python; - Опыт работы с LLM в проде (не только pet-проекты); - Понимание prompt engineering, RAG-подходов, оценки качества моделей; - Опыт деплоя ML/LLM решений (Docker, Kubernetes, cloud); - Работа с векторными базами данных. Будет плюсом: - Fine-tuning / PEFT (LoRA, adapters); - RLHF или synthetic data generation; - Опыт с LangChain, LlamaIndex, Hugging Face; - Оптимизация инференса (vLLM, TensorRT, ONNX); - Опыт с мультимодальными моделями; - Публикации / open-source / pet-проекты; - Участие в хакатонах; - Понимание продуктовых метрик. Условия: - Полная удалёнка; - Аккредитованная ИТ-компания; - Интересный продукт, современный стек, микросервисная архитектура; - Известные клиенты из ТОП-10 в своих сегментах; - Прокачка скиллов под руководством лидов с 8+ лет опыта; - Достойный оклад по итогам собеседования + ежеквартальный сбор ОС с премированием лучших сотрудников; - Возможность взять на себя дополнительные задачи за соответствующее вознаграждение; - Премии за менторство младших сотрудников; - Ежеквартальные бонусы и подарки лучшим сотрудникам; - Пригласите друга и получите щедрое вознаграждение за рекомендацию.

Location: Armenia; Spain; Serbia; Poland; Kyiv City; Kyiv; Odesa; North Macedonia Workplace: remote About Provectus Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.   Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.   Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDE and an FDX, Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs. Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution. Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails.  You'll have real ownership of components and of the technical decisions inside them. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket Excellent communication and problem-solving skills Comfort with ambiguity and ownership. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth 5+ years in software or ML engineering, with production systems you were accountable for . Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes. Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks. Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production. Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive. Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release. Model and agent monitoring, drift detection. Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs. Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus. MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus. Responsibilities: Work in a pair with an FDE and an FDX. Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Build and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Integrate AI components into backend services and RESTful APIs Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints Participate in technical discussions and architectural decisions Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops. Nice to Have: Experience in one of the industries: financial services, insurance, healthcare. Consulting, professional services, or other embedded customer-facing delivery. AWS and Claude Code Certifications A2A: you can explain agent-to-agent interoperability CI/CD pipeline experience (GitHub Actions, GitLab CI) Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Experience in an additional language (Go, TypeScript, or Rust). Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside FDE and FDX A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire:   Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant  HR Interview. Soft skills and expectations HM interview. Tech questions; a live engine

Задачи: — анализ банковских и крипто-транзакций для выявления мошенничества и AML-сценариев — разработка и улучшение ML-моделей для fraud detection — graph analysis / Graph ML (GNN, node embeddings, link prediction) — вывод моделей в production Требования: — опыт работы с transactional fraud / AML от 5 лет — strong Python + ML stack (PyTorch / TensorFlow) — опыт с Graph ML / Neo4j / NetworkX / GNN — production ML и ETL pipelines Будет плюсом: — MLOps — ClickHouse / Elasticsearch / Cassandra — опыт с real-time systems и асинхронным Python Условия: — релокация (Узбекистан / Дубай / ЕС) или B2B outside РФ/РБ/Украины — зарплата в валюте — relocation package — международная русскоговорящая команда — сложные ML/AI задачи и сильная technical culture

Требования: - отличное владение русским языком - внимательность, аналитичность - способность работать по инструкции Обязанности: - проверять соответствие описания видео - вносить корректировки и уточнения - следовать инструкциям и метрикам качества Условия: - оплата от 30 000 рублей - гибкий график: 5/2, 6/1, 7/0 - работать можно в любое время - норма — от 6 часов в день - компания компенсирует налоги - удалённая работа - возможность карьерного роста

Location: Canada; Europe; United States; United Kingdom Workplace: Remote 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! Why this role? Ship state of the art models to production. Design and implement novel research ideas. Build elegant training/deployment pipelines. Join us at a pivotal moment, shape what we build and wear multiple hats as an intern! Our recruitment process will begin in the upcoming weeks, and we will be carefully reviewing applications and assessing potential candidates for our internships. Should we find a suitable match with your qualifications and our requirements, we will be in touch to discuss the opportunity further and to advance your application to the next stage Please Note: To be eligible for this position you should be a student currently enrolled in a post-secondary program, available for a full-time 3-6 month internship, co-op, or research work term. As a Machine Learning Intern, you will: - Design, train and improve upon cutting-edge models. - Help us develop new techniques to train and serve models safer, better, and faster. - Train extremely large-scale models on massive datasets. - Explore continual and active learning strategies for streaming data. - Learn from experienced senior machine learning technical staff. - Work closely with product teams to develop solutions. You may be a good fit if you have: - Proficiency in Python and related ML frameworks such as Tensorflow, TF-Serving, JAX, and XLA/MLIR. - Experience using large-scale distributed training strategies. - Familiarity with autoregressive sequence models, such as Transformers. - Strong communication and problem-solving skills. - A demonstrated passion for applied NLP models and products. - Bonus: experience writing kernels for GPUs using CUDA. - Bonus: experience training on TPUs. - Bonus: papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP). 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: Canada; San Francisco; Europe; London; United States; United Kingdom; Toronto; Montreal Workplace: Remote 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! Why this role? To have the opportunity to collaborate with Cohere researchers and tools on designing and implementing novel research ideas and shipping state-of-the-art models to production. We have openings in teams covering base model training, retrieval augmented generation, data and evaluation, safety, and finetuning, to name a few; and we are open to receiving intern applications in any research area relating to LLMs to broaden your research connections while obtaining deep experience in a growing AI startup.  Please Note: To be eligible for a Research Internship, you must be currently pursuing a PhD in Machine Learning, NLP, or a related discipline. You need to be available for a full-time internship that lasts for 4-6 months. As a Cohere Research Intern, you will: - Conduct cutting-edge machine learning research, building and training large language models.  - Focus on research projects aimed at expanding the frontier of knowledge in language modelling and associate areas such as evaluation, multimodal models, optimisation etc. - Disseminate your research results through the production of publications, datasets, and code. - Contribute to research initiatives that have practical applications in Cohere’s product development.  You may be a good fit if you: - Are currently pursuing, or in the process of obtaining, a PhD in Machine Learning, NLP, Artificial Intelligence, or a related discipline. We will also consider exceptional non-PhD candidates. - Are eligible for work authorization in the country of employment at the time of hire and maintain ongoing work authorization throughout the internship period.  - Have experience using large-scale distributed training strategies, data annotation and evaluation pipelines, or implementing state of the art ML models. - Are familiar with autoregressive sequence models, such as Transformers. - Have strong communication and problem-solving skills with the ability to convey complex research findings clearly and succinctly.  - Have knowledge, or are knowledgeable, of programming languages such as Python, C, C++, Lua, or related languages. - Have knowledge of related ML frameworks such as JAX, Pytorch and Tensorflow. - Have previous experience in building systems based on machine learning and deep learning techniques.  - Demonstrate passion for applied NLP models and products. Preferred Qualifications: - Demonstrated expertise through publications in top tier venues in fields such as machine learning, NLP, artificial intelligence, computer vision, optimization, computer science, statistics, applied mathematics, or data science.  - Proven ability to tackle analytical problems using quantitative methodologies.  - Proficiency in handling and analysing complex, high-dimensional data from various sources. - Experience in applying theoretical and empirical research to real-world problem-solving. 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.

Задачи: — Участие в разработке и деплое ML/LLM решений. — Применение Prompt Engineering и методов Fine-tuning/PEFT. — Построение RAG пайплайнов и работа с векторными базами данных. — Оптимизация производительности LLM и оценка их качества. — Разработка с использованием мультимодальных моделей. Требования: — Уверенное владение Python. — Опыт деплоя ML/LLM решений с использованием Docker, Kubernetes, облачных платформ AWS, GCP, Azure. — Опыт работы с Prompt Engineering (few-shot, chain-of-thought, ReAct). — Знание Fine-tuning / PEFT (LoRA, QLoRA, adapters). — Опыт построения RAG пайплайнов (chunking → embedding → retrieval → генерация). — Умение работать с векторными базами данных (Qdrant, Pinecone, Milvus, FAISS). — Опыт RLHF или работы с синтетическими данными (предпочтения, генерация и фильтрация). — Знание фреймворков LangChain, LlamaIndex, Hugging Face. — Опыт оценки LLM (BLEU, ROUGE, MMLU, кастомные эвалы). — Опыт оптимизации инференса (vLLM, TensorRT, ONNX). — Опыт работы с мультимодальными моделями (LLaVA, GPT-4V, Qwen-VL). — Грейд Middle/Middle+. Условия: — Полная занятость. — Удалённый формат работы. — Интересные проекты в области ИИ.

Задачи: — Конфигурировать и разворачивать инфраструктурные компоненты — Адаптировать/разрабатывать с нуля автоматизированные пайплайны под актуальные бизнес-задачи (AutoML, LLM, computer vision и пр) — Создавать и поддерживать пользовательские инструменты для работы в едином ML-контуре применения и обучения — Масштабировать разрабатываемые системы и инструменты для управления жизненным циклом моделей машинного обучения Требования: — Опыт DevOps от 3х лет (Docker, Helm, Jenkins/GitLab CI, Python) — Опыт ML/MLOps от 1 года (Airflow, JupyterHub, MLflow, Seldon, CUDA) — Опыт администрирования Kubernetes от 2х лет — Опыт работы с Hadoop, Spark, Kafka, ELK Условия: — Работа в аккредитованной IT компании, имеющей собственные продукты — Формат работы: удаленный по РФ — Предоставляем расширенный ДМС (со стоматологией) — Выдаем необходимую для работы технику (ноутбук) — Возможность воспользоваться реферальной программой: “Приведи товарища” — Участие в Бонусной программе — Помогаем в ипотеке и других пособиях — Предоставляем возможность построить карьеру в рамках ведущих компаний России

Location: Tbilisi; Belgrade; Budapest; Madrid; Riga; Tallinn; Valencia; Vilnius; Yerevan Workplace: remote About us: Planner 5D is a global hub for home design, uniting over 100+ million users in 230 territories around our planet (yes, we have 2 users connecting from Antarctica each month!). Our platform simplifies the home renovation process from concept to execution, and this process has never been more straightforward with our cutting-edge software. You don't need to be a professional designer to enhance your home or workspace. We are dedicated to streamlining the design experience, making the customization of dream homes accessible to everyone. Our cross-platform presence spans Web, iOS, Android, Windows, and MacOS, ensuring seamless synchronization for our users.  Planner 5D is more than just software; it's a vibrant community of enthusiastic and product-oriented professionals. From diverse backgrounds, our team collaborates across time zones, fueled by a shared passion to revolutionize the home design market. We are constantly looking for like-minded talent eager to take ownership and drive innovation within our expanding suite of tools. Join us in shaping the future of home design. We are searching for an AI / Machine Learning Researcher to join our AI team and focus on applied research for real-world product challenges. You will work with raw data, explore new approaches, formulate and test hypotheses, and build prototypes and MVP solutions that can later become production features within the Planner 5D platform. You will: Conduct applied research in AI/ML to solve real-world problems related to home design, 2D/3D data, and visual understanding. Work with raw and unstructured data (images, 3D point clouds, text, video) to identify useful signals and formulate research directions. Generate and test hypotheses through experiments, modeling, and data analysis. Design and implement prototypes and MVP solutions, validating ideas before production implementation. Train, evaluate, and iterate on machine learning and deep learning models. Contribute to the development of AI-driven features, including computer vision systems and AI agents. Collaborate with engineers to translate research prototypes into production-ready solutions. Provide algorithm-level support for deployed models, improving and adapting them as new data or requirements emerge. Requirements 4+ years of experience in AI/ML research or applied machine learning. Strong background in mathematics and computer science, including statistics, optimization, linear algebra, and algorithms. Solid understanding of machine learning and deep learning methods. Experience working with real-world datasets, including data exploration, cleaning, feature extraction, and experimental validation. Proficiency in Python and common scientific/ML libraries (NumPy, SciPy, Pandas, etc.). Experience with deep learning frameworks such as TensorFlow or PyTorch. Familiarity with computer vision or multimodal AI (images, video, 3D data such as point clouds and meshes). Ability to independently formulate research hypotheses, design experiments, and evaluate results. Strong analytical thinking and ability to solve complex problems with limited prior solutions. Nice to have Experience with 3D data processing (point clouds, meshes, spatial data). Experience building or experimenting with AI agents or LLM-based systems. Experience training and fine-tuning neural networks. Interest in geometry, graph theory, or computational geometry. What we offer: - Great work environment that balances freedom and responsibility - Our team is made up of friendly and passionate enthusiasts who are eager to help and share their knowledge - We believe in work-life balance, so you'll receive around 30+ days of paid holidays per year - Company computer equipment by request - Education courses for our team members, and we're willing to allocate funds toward your training expenses - English classes on our corporate platform - Offline company events in Lithuania and online team-building - Unlimited free Planner5D subscriptions

О ПРОЕКТЕ DigiNavi — это иммерсивная AR-навигация для крупных общественных пространств (ТЦ, аэропорты). Мы превращаем локации в SMART-зоны с помощью Computer Vision и дополненной реальности. Наша команда внедряет подход VibeCoding: вы выступаете архитектором решений, используя AI-агентов (Claude Code, Cursor) как основной инструмент исполнения. ЧЕМ ПРЕДСТОИТ ЗАНИМАТЬСЯ — AI-Driven Development: Разработка и поддержка кода через AI-инструментарий. — R&D & Prototyping: Быстрая проверка гипотез и внедрение алгоритмов (SuperPoint, NetVLAD, Gaussian Splatting). — Architecture: Рефакторинг CV-компонентов в гетерогенной среде (Go / C++ / Python). — Integration: Внедрение ML-моделей в микросервисную архитектуру через gRPC и Kafka. — Optimization: Работа с high-load системами и оптимизация инференса моделей. ЧТО МЫ ЖДЕМ ОТ ВАС — CV & ML Expert: Глубокие знания PyTorch и OpenCV. Опыт создания Computer Vision решений. — VibeCoding Skills: Практический опыт работы с Claude Code, Cursor или аналогами. Понимание SDD и TDD подходов. — Backend: Опыт production-развертывания (FastAPI, Docker) и понимание микросервисов. — Polyglot Mindset: Готовность работать в окружении, где используются Go и C++ (чтение и интеграция). — Документация: Умение создавать техническую документацию и структурировать процессы там, где их нет. БУДЕТ ПЛЮСОМ — Опыт оптимизации инференса для мобильных платформ. — Работа с облачной инфраструктурой GCP. — Понимание специфики разработки в сфере AR/VR.

Задачи: — Разрабатывать и внедрять ML-модели в областях Computer Vision и NLP. — Развёртывать и оптимизировать inference-пайплайны на GPU-серверах. — Работать над мультимодальными AI-фичами (текст, изображения, видео, аудио). — Масштабировать производительность моделей под растущую базу пользователей. — Сотрудничать с backend-инженерами над продакшн-API. — Проводить эксперименты и A/B-тесты на основе реальных пользовательских данных. Требования: — 5+ лет практического опыта в ML/AI-разработке. — Отличное знание Python и фреймворков PyTorch / TensorFlow. — Глубокое понимание NLP (LLMs, embeddings, transformers) и CV (image generation, diffusion models, video processing). — Опыт работы с GPU-обучением и inference. — Навыки оптимизации моделей (quantization, distillation, batching, caching). — Опыт продакшн-деплоя ML-сервисов. — Будет плюсом: опыт работы с видео-AI моделями, понимание high-load inference систем, опыт с ML-мониторингом и аналитикой (Weights & Biases, MLflow, Grafana), опыт в сфере conversational AI или чатботов, участие в open-source, Kaggle или публикации в ML-сообществе. Условия: — Работа в международной продуктовой компании. — Гибкий график и удалённый формат. — Возможность влиять на ключевые продуктовые решения.

Задачи: — Доработка алгоритмов CV/ML — Разработка и поддержка бэкенда — Разработка фронтенда (минимально) — Рефакторинг и оптимизация кодовой базы — Участие в полном цикле разработки: обсуждение, реализация, тестирование, запуск в продакшн Требования: — Уровень: Senior (коммерческий опыт от 5 лет) — Python (Core): глубокое знание языка и экосистемы — Computer Vision (CV): практический опыт с OpenCV и другими библиотеками для обработки изображений/видео, понимание технологий, наличие минимального опыта — Machine Learning (ML): опыт работы с фреймворками (TensorFlow, PyTorch или аналогами) для задач классификации, детекции, трекинга — Backend (Node.js): уверенное владение Node.js и фреймворками (Express.js, Nest.js) (желательно, но не критично) — Базы данных: опыт работы с реляционными (PostgreSQL) и/или NoSQL (MongoDB) СУБД — Git: уверенное владение системой контроля версий — Желательно: опыт работы с видеопотоками (RTSP-протокол), Frontend (JavaScript) с Vue, Docker и основы DevOps, понимание принципов работы message brokers (RabbitMQ, Kafka), опыт оптимизации кода и ML-моделей для работы в реальном времени, опыт работы с Kubernetes Условия: — Оформление: ТД / ИП / СЗ — Полная занятость — Работа над проектом по системе AI видеоаналитики — Стек: FastAPI, ONNX Runtime / TensorRT, OpenCV, FFmpeg, MS, Meta, MLflow, Docker, Docker Compose (лучше кубы), YOLO, ResNet, MobileNet, ONNX, MLflow, NMS (Non-Maximum Suppression), JSON, Webhook или очереди, queue, threading/asyncio, REST API, оценка качества (loss, accuracy, confusion matrix и др) — Будет плюсом наличие собственных моделей, пригодных к дообучению под задачи проекта.

Задачи: — Разработка и оптимизация моделей для обработки/генерации звука. Требования: — Минимум 3 года работы в разработке и оптимизации моделей для обработки/генерации звука; — Глубокие знания в области DL и ML; — Опыт работы с инструментами и библиотеками TensorFlow, TensorRT, Triton (NVidia), PyTorch, Hugging Face; — Умение работать в команде и проактивная позиция; — Знание архитектуры моделей (Transformers, RNN и др.); — Опыт работы с большими данными и основами обработки данных; — Умение оптимизировать модель и проводить её оценку (A/B тестирование, метрики качества). Желательно: — Опыт работы над задачами speech enhancement, noise cancellation, tts, asr. Условия: — Оформление: Только ИП.

Location: Remote Workplace: Remote We are building AI to simulate the world through merging art and science. We believe that world models are at the frontier of progress in artificial intelligence. Language models alone won’t solve the world’s hardest problems – robotics, disease, scientific discovery. Real progress requires models that experience the world and learn from their mistakes, the same way that humans do. And this kind of trial and error can be massively accelerated when done in simulation, rather than in the real world. World models offer the most clear path to general-purpose simulation, changing how stories are told, how scientific progress is made and how the next frontiers of humanity are reached. Our team consists of creative, open minded, caring and ambitious people who are determined to change the world. We aspire to continuously build impossible things and our ability to do so relies on building an incredible team. If you are driven to do the same, we'd love to hear from you. *Titles posted to our careers page are updated regularly to best advertise the role and expected seniority, but internally we have a flat titling system with two types of titles: one for individual contributors and one for people managers. ABOUT THE ROLE We’re looking for Research Engineers to contribute to our ambitious research agenda spanning multimodal foundational models, interactive world modeling, and real-time generation. This role is highly collaborative and will touch many aspects of our broader research efforts, taking a full-stack approach across pretraining, SFT/RL post-training techniques, evaluation, and bringing models to production. WHAT YOU’LL DO - Run experiments to teach world models new behaviors — action following, scene manipulation, camera control, and beyond - Develop and test new data strategies, architectural variants, and training techniques - Design evaluations that measure model capabilities, and use them to drive model improvement - Take features from research prototype to production, collaborating with product and creative teams to address application-specific gaps - Contribute across the entire stack (data pipelines, modeling, production inference) to move projects forward WHAT YOU’LL NEED - 4+ years of experience in machine learning research or engineering - Familiarity with the architecture, training, and inference of large-scale multimodal generative models - Experience building robust data pipelines for pretraining and post-training - Comfort working across the full research stack: data, training, evaluation, and deployment - Proficiency with at least one ML framework (e.g. PyTorch, JAX) and experience with distributed training at scale - Ability to context-switch quickly and drive projects forward in a fast-moving, ambiguous environment - Excitement about building AI that simulates the world Runway strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on competitive market rates for our size, stage and industry, and salary is just one part of the overall compensation package we provide. There are many factors that go into salary determinations, including relevant experience, skill level and qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range. Lastly, the provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range, which again, will be communicated to candidates. WORKING AT RUNWAY Great things come from great teams. https://www.youtube.com/watch?v=kwmj4ato2kw&ab_channel=Runway We’d love to hear from you. We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. So regardless of race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply. More about Runway - Universal World Simulator https://runwayml.com/world-simulator.html - GWM-1 https://runwayml.com/research/introducing-runway-gwm-1 - Gen-4.5 https://runwayml.com/research/introducing-runway-gen-4.5 - General World Models https://runwayml.com/research/introducing-general-world-models - Robotics SDK https://runwayml.com/research/introducing-runway-gwm-1#robotics-section - Conversational Real-time Agents https://runwayml.com/research/introducing-runway-gwm-1#avatars-section - Runway Studios https://runwayml.com/studios We're excited to be recognized as a best place to work: Crain's https://www.crainsnewyork.com/awards/best-places-work-2023 | InHerSight https://www.inhersight.com/companies/best/city/new-york-city-ny | BuiltIn NYC https://builtin.com/awards/new-york-city/2024/best-places-to-work | INC https://www.inc.com/best-workplaces/2024

Location: Toronto; London; New York; Paris; Montreal Workplace: Remote 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: As a Senior Machine Learning Engineer specializing in synthetic data, you will play a pivotal role in developing the synthetic data pipeline that is crucial to Cohere’s advanced language models. Your responsibilities will encompass the end-to-end management of synthetic data, including maintaining and optimizing the synthetic data pipeline, data analysis and generation, as well as conducting data ablations and model evaluation to gauge data quality. You will work with diverse web data and code data and transform them using generative models to improve token efficiency and model quality. By combining research and engineering, you will bridge the gap between raw data and cutting-edge AI models, directly contributing to improvements in critical training metrics like throughput and accelerator utilization. Your work will be essential to Cohere’s mission of delivering efficient and reliable language understanding and generation capabilities, driving innovation in natural language processing. If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a meaningful impact. Note: We have offices in London, Toronto, New York and San Francisco, but we’re also remote-friendly! This team operates primarily between ET to CET time zones, so we’re seeking candidates in locations that align with these hours for effective collaboration. Key Responsibilities: - Design and build scalable inference pipelines that run on large GPU clusters. - Conduct data ablations to assess data quality and experiment with data mixtures to enhance model performance. - Research and implement innovative synthetic data curation methods, leveraging Cohere’s infrastructure to drive advancements in natural language processing. - Collaborate with cross-functional teams, including researchers and engineers, to ensure data pipelines meet the demands of cutting-edge language models. Qualifications: - Strong software engineering skills, with proficiency in Python and experience building data pipelines. - Familiarity with data processing frameworks such as Apache Spark, Apache Beam, Pandas, or similar tools. - Experience working with LLMs through work projects, open-source contributions or personal experimentation. - Familiarity with LLM inference frameworks such as vLLM and TensorRT. - Experience working with large-scale datasets, including web data, code data, and multilingual corpora. - A passion for bridging research and engineering to solve complex data-related challenges in AI model training. Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).   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: New York; San Francisco; Toronto; Montreal Workplace: Remote 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: Our team is a fast-growing group of committed researchers and engineers. The mission of the team is to build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. As an engineer on this team, you will work on advancing core audio model serving metrics, including latency, throughput, and quality by diving deep into our systems, identifying bottlenecks, and delivering creative solutions for audio processing and streaming workloads. You’ll collaborate closely with both the training and serving infrastructure teams to ensure seamless integration between model development and deployment, with a special focus on real-time and streaming audio inference. Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations. Qualifications: - Significant experience developing high-performance audio or machine learning inference systems. - Proficiency with programming languages such as C++ and Python. - Hands-on experience with deep learning models for audio, speech, or language applications. - A bias for action and a strong results-oriented mindset. It is a big plus if you also have considerable experience with: - GPU programming, low-level system optimization, model parallelization techniques over multiple GPUs - Have experience with duplex real-time streaming architectures. - Internals of machine learning frameworks for audio (such as PyTorch, TensorFlow, or specialized audio libraries). - Have experience with inference framework like vLLM, SGLang, Tensort-LLM, or custom distributed inference systems - Sequence modeling (e.g., transformers for audio/speech) and end-to-end audio pipeline optimization 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: New York; San Francisco; Toronto; Montreal Workplace: Remote 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: Large Language Models (LLMs) continue to push the boundaries of what AI systems can do — but inference is still the bottleneck. The Model Efficiency team is responsible for pushing the limits of LLM inference efficiency across our foundation models. We explore and ship breakthroughs across the model execution stack, including: - model architecture and MoE routing optimization - decoding and inference-time algorithm improvements - software/hardware co-design for GPU acceleration - performance optimization without compromising model quality Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations. Key Responsibilities: As a Staff Research Engineer, you will develop, prototype, and deploy techniques that materially improve how fast and efficiently our models run in production. Qualifications: - Have a PhD in Machine Learning or a related field - Understand LLM architecture, and how to optimize LLM inference given resource constraints - Have significant experience with one or more techniques that enhance model efficiency - Strong software engineering skills - An appetite to work in a fast-paced high-ambiguity start-up environment - Publications at top-tier conferences and venues (ICLR, ACL, NeurIPS) - Passion to mentor others 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.