Данные и ML вакансии

Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition). Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.

• Own AI features from concept to production in a high-load environment (50k RPS, 450M users). • Work with LLMs and train purpose-built models for real-time applications.

Ищу Senior Data Engineer, который сможет взять на себя развитие нового DWH / Lakehouse-контура и стать владельцем хранилища данных end-to-end. — перенести текущую аналитику на новую Lakehouse-архитектуру;

Location: San Francisco, CA Workplace: Hybrid ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE ROLE We're looking for a Deployed Engineer to join our Professional Services team, working directly with enterprise customers to build reliable, production agents. You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution, or build it for them. You might spend a week designing a customer's agent architecture, a few weeks co-building their evaluation pipeline, or a quarter embedded inside their team shipping alongside their engineers. You are someone who's built real AI systems for production and can defend the technical tradeoffs within them. KEY RESPONSIBILITIES - Advising: Agent architecture design, evaluation strategy review, and best-practice production guidance. - Building: Co-build with the customer's engineering team across the full Agent Development Lifecycle (ADLC) in outcome-scoped engagements. - Embedding: Serve as a deployed engineer inside the customer's team for extended engagements, operating as a de facto member of their org to ship agent systems directly. - Agent Engineering: ADLC end-to-end, architecture design, orchestration patterns, evals, custom conversational UIs, and production deployment. - Applied AI: Post-training, supervised fine-tuning, harness engineering, trace mining, model selection and evaluation methodology. REQUIREMENTS - 4+ years of software engineering experience with deep expertise in Python. TypeScript/JavaScript a plus. - 2+ years of hands-on experience building and shipping production agent systems. - Strong client-facing communication skills, with the ability to confidently articulate architectural decisions to technical stakeholders (engineers, architects, CTOs). - Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks, including multi-agent patterns and state management (short and long-term memory). - Deep familiarity designing and implementing evaluation methodologies for non-deterministic AI systems. - Comfortable operating across the full spectrum from advisory to embedded delivery. - Willing to travel up to 20% of the time. NICE TO HAVE - Exposure to dataset curation and post-training techniques (SFT, DPO, RLHF) on open-weight models using tools like Axolotl, Unsloth, Hugging Face transformers, or TRL. - Experience with trace mining to drive continuous improvement loops LOCATION San Francisco, CA COMPENSATION $150,000-$300,000 base + equity Looking to lead technical strategy across the customer journey from Proofs of Value (PoVs) to driving overall adoption? Please check out the Deployed Engineer listing focused on Account Strategy & Solutions. Compensation Philosophy: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

О нас: Наш клиент — аутстафф-компания, подбирающая инженеров на длительные проекты. Сейчас ищем Data Scientist / AI Engineer. Про проект: Заказчик развивает ИИ-направление и выводит решения на базе LLM в промышленную эксплуатацию. Нужны специалисты, которые доводили AI-агентов до продакшена и понимают практические аспекты. Команда распределённая, работа полностью удалённая. Кого ищем: Data Scientist / AI Engineer с коммерческим опытом от 3 лет, реализовавший и выведший в продакшн решения на базе ИИ, включая AI-агентов на базе LLM. Уверенный в классическом ML, глубоком обучении и инфраструктуре. Задачи: — Разрабатывать и выводить в промышленную эксплуатацию решения на базе ИИ, включая AI-агентов на базе LLM. — Проектировать архитектуру AI-агентов и подходы к их построению. — Работать с Deep Learning в части NLP, включая архитектуру Transformer. — Делать fine-tuning языковых моделей (GPT-like, BERT-like и др.). — Разрабатывать интеграции с ИТ-системами. — Работать с промышленным Kubernetes. Требования: — Коммерческий опыт работы от 3 лет. — Опыт реализации и вывода в промышленную эксплуатацию решений на базе ИИ, включая AI-агентов на базе LLM, в роли Data Scientist / AI Engineer. — Знание основ классического машинного обучения. — Хорошее знание Deep Learning (NLP). — Глубокое понимание архитектуры Transformer. — Глубокое понимание подходов и методов проектирования AI-агентов. — Практический опыт fine-tuning языковых моделей (GPT-like, BERT-like и др.). — Уверенная работа со стеком Python — Torch, Transformers. — Опыт разработки интеграций с ИТ-системами. — Опыт промышленной работы с Kubernetes. Обязательные технологии и инструменты: Python, Torch, Transformers, Kubernetes. Отбор: Техническое собеседование по каждому пункту требований. Условия: — Полная удалёнка. — Зарплата от 200 000 до 230 000 ₽ на руки. — Выплата раз в месяц. — Оформление по ИП, налоги покрывают. Как откликнуться: В отклике кратко рассказать о своём опыте с LLM и AI-агентами, роли, стеке, кейсах с fine-tuning и промышленным Kubernetes.

• Разработка и вывод в продакшн data-приложений на Python/Streamlit в Databricks Apps. • Доработка Lineage Explorer до полной готовности к продакшну.

Ищем CV-инженера в облачную платформу, которая помогает рентгенологам описывать КТ и МРТ быстрее и точнее. Основной проект — генерация заключений по КТ органов грудной клетки: от классических 3D-сервисов сегментации и классификации патологий до использования LLM и обучения VLM для КТ.

Location: Ukraine; Spain; Serbia; Yerevan; Poland Workplace: remote 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 senior pod alongside an FDE, an FDX, and Senior AI Engineers — contributing to real delivery work while building toward independent ownership. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket. Excellent communication and problem-solving skills. Comfortable with some ambiguity, with support from senior team members as you take on more. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth Hands-on experience building or contributing to RAG systems, ideally in a production or near-production setting. Solid engineering fundamentals; Python and/or TypeScript proficiency. Productive in an unfamiliar codebase with some ramp-up support. Practical AWS experience (Lambda, S3, ECS, or similar); ready to grow into Bedrock and Bedrock AgentCore . Some experience with containers and CI/CD in real projects. Exposure to evaluating non-deterministic systems — you've contributed to or run test/eval cycles, even if you haven't owned a full eval suite end-to-end. Basic working knowledge of model/agent monitoring concepts. Awareness of cost and latency trade-offs when working with LLMs. Some hands-on exposure to the Claude ecosystem (Claude Code, CLAUDE.md, hooks, skills files) is a plus, or strong ability to ramp up quickly. Practical experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) in real projects. 2+ years of software or ML engineering experience, including some exposure to production systems. Solid AI/ML foundations — you understand what the models do well enough to reason about common failure modes. 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 (or actively pursuing them). A2A: Interest in agent-to-agent interoperability concepts. 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. 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. MCP: you can say why an agent would prefer it to a REST integration, having authored a server is an additional  plus. Responsibilities: Build and contribute to RAG system components under senior guidance, with growing autonomy. Write tests and help build out evaluation harnesses for the features you work on. Write production code across the stack (AI, backend services, data pipelines) with code review support. Help integrate AI components into backend services and RESTful APIs. Support deployment of systems to AWS (containerized, CI/CD), taking on more of this independently over time. Contribute to documentation, runbooks, and client handover materials. Participate in technical discussions and architectural decisions, with an eye toward taking on more of this independently. Support model evaluation efforts and help investigate and improve failure modes. Take on increasing ownership of components and technical decisions as you grow in the role. 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 engineering session is also possible

Ищем LLM / ML Engineer с практическим опытом обучения языковых моделей. Задача — обучение с нуля специализированных моделей на 1–10 млрд параметров.

We're launching a new business line in Colombia and looking for someone to become the first dedicated Data Scientist in our Risk team, building the function from the ground up as part of this launch.

Ищем ML Engineer для развития ML-части e-commerce поиска: vector search, reranking, query understanding и ranking на основе click/conversion signals.

- Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог; - Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.);

4+ года коммерческого опыта в Data Engineering. Уверенное знание AWS: S3, Glue, Athena, Lake Formation/IAM, Lambda, Step Functions.

• 2+ года в Data Engineering / DWH. • Работа с большими объёмами данных.

LLM/NLP: LLM/SLM, prompt engineering, few-shot, BERT, PEFT, LoRA/QLoRA. Voice AI: дообучение TTS, ASR (GigaAMv2), VAD, streaming ASR/TTS.

Wincent is looking for a Quantitative Researcher to generate research ideas, improve trading strategies, model and test strategies, and monitor production trading.

Wincent is looking for a Quantitative Research Intern to analyze market signals, develop trading strategies, construct quantitative models, and participate in trading exercises.

— Разрабатывать CV/ML-решения для преобразования чертежей в структурированные инженерные данные. — Работать с OCR, raster/vector geometry, CAD/BIM и пространственными связями.