Вакансии

Задачи: — Владеть AI-функциями от концепции до производства в масштабе 50k RPS и 450M пользователей. — Создавать большие языковые модели (LLM) и обучать кастомные модели для приложений в реальном времени. — Тесно сотрудничать с продакт-менеджерами для трансформации целей в решения. — Оценивать и внедрять мониторинг производительности моделей, отслеживать дрейф и регрессии. Требования: — Опыт работы инженером 5+ лет с практическим опытом LLM в продакшене. — Умение обучать и выпускать модели на неструктурированных данных. — Глубокое понимание оценки моделей и мониторинга в продакшене. — Владение Python и основами машинного обучения. — Английский язык на уровне Intermediate и выше. Условия: — Конкурентная зарплата, бонусы и опционы (каждый сотрудник — партнер). — Официальное трудоустройство на Кипре с поддержкой визы для сотрудника и супруга. — Комфортный офис в Лимассоле с питанием и перекусами. — Медицинская страховка. — Доступ к матчам и мероприятиям Aris Limassol F.C. — Возможности профессионального роста и командных активностей.

Задачи: — Классический ML на табличных данных: бустинги (CatBoost/XGBoost/LightGBM), feature engineering, работа с признаками и метриками, валидация — Работа с Python и SQL на продовом уровне (не только ноутбуки) — Понимание статистики, умение обосновать выбор модели и метрики Плюсом будет: — Опыт в графовом ML: GNN, graph embeddings, link prediction, PyG/DGL/NetworkX — Опыт в смежных направлениях: сетевой анализ, антифрод, рекомендации, маршрутизация — Работа с пространственными данными: OSM, PostGIS, H3/geohash, дорожные графы

Location: Москва **ИТ B2C** — самая крупная экосистема в Сбере. Нас более 8000 человек в 18 городах России. Мы занимаемся разработкой и развитием розничных решений, помогая сделать сервисы Банка доступнее, безопаснее и удобнее. Ждем именно тебя! Мы — команда экспертов, объединённых общей страстью к искусственному интеллекту и рекомендательным системам. Наша главная задача — создание современной масштабируемой рекомендательной платформы, способной предвосхищать ожидания пользователей и предлагать персонализированные рекомендации на каждом этапе их взаимодействия с экосистемой Сбер. Наши решения охватывают широкий спектр отраслей: от финансов и e-commerce до индустрии развлечений и здравоохранения. Мы следим за развитием технологий, экспериментируем с новыми ML-подходами, внедряем их в платформу и доводим до конкретного применения в бизнесе. Мы ищем **Lead ML Engineer**, который будет отвечать за технологическое развитие направления рекомендательных систем и формирование единой инженерной и операционной среды для DS- и MLE-команд. В зоне ответственности — скорость и воспроизводимость проверки DS-гипотез, качество разработки и вывода моделей в production, а также надёжная эксплуатация ML-решений с предсказуемыми качеством и стоимостью. Обязанности * формировать технологическое видение и roadmap развития ML Engineering и MLOps в направлении рекомендательных систем, определяя ключевые инженерные приоритеты и точки роста * развивать единый жизненный цикл ML-решений — от формулирования и воспроизводимой проверки DS-гипотез до запуска моделей в production, мониторинга и дальнейшего сопровождения * создавать инженерные стандарты и практики, которые помогают командам выпускать качественные и надёжные модели: требования к коду и данным, тестированию, версионированию, документированию, релизам * управлять портфелем технологических инициатив, находя баланс между развитием новых возможностей, повышением надёжности и устранением архитектурных ограничений * повышать производительность DS/MLE команд за счёт автоматизации, стандартизации, переиспользуемых компонентов и упрощения пути от эксперимента до production * развивать применение GenAI в процессе создания ML-решений: находить наиболее перспективные сценарии, запускать пилоты и оценивать их влияние на скорость и качество разработки * повышать масштабируемость, надёжность и экономическую эффективность ML-инфраструктуры * развивать ML-инженеров и технических лидеров направления. Требования * опыт технического лидерства в крупном ML-направлении не менее двух лет: умение формировать технологическое видение и roadmap, а также доводить системные изменения до результата * глубокое понимание полного жизненного цикла ML-решений — от эксперимента и обучения модели до production, мониторинга, масштабирования и дальнейшего сопровождения * опыт проектирования и развития высоконагруженных ML-систем, включая batch-, streaming- и real-time-сценарии * опыт взаимодействия с продуктовыми, платформенными и инфраструктурными командами, включая согласование приоритетов и защиту потребностей своего направления * умение принимать решения в условиях конфликтующих приоритетов, объяснять компромиссы и доводить выбранные инициативы до результата * опыт people management в DS/MLE командах: найм и адаптация сотрудников, постановка целей, регулярная обратная связь, performance review, развитие и удержание специалистов, а также формирование среды для обмена знаниями и распространения сильных инженерных практик * опыт внедрения GenAI-инструментов в процессы разработки и оценки их влияния на скорость и качество работы команд. **Будет большим плюсом:** * опыт работы с рекомендательными системами, поиском, ранжированием, персонализацией или рекламными технологиями * практический опыт работы в роли Data Scientist: формулирование и проверка ML-гипотез, оценка качества моделей и участие в A/B-тестах. * опыт эксплуатации real-time ML-систем на масштабе миллионов пользователей и при строгих требованиях к latency и availability. **Стек технологий:** Python, PySpark, PyTorch, RePlay, GigaChat, Airflow, MLflow, Kubernetes, Redis, Kafka, S3, FastAPI и другие инструменты для разработки, обучения, внедрения и мониторинга ML-моделей. Условия * комфортный современный офис рядом с м. Кутузовская * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития   * расширенный ДМС, льготное страхование для семьи и 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

Задачи и требования: — Классический ML на табличных данных: бустинги (CatBoost/XGBoost/LightGBM), feature engineering, работа с признаками и метриками, валидация — Python и SQL на продвинутом уровне (не только ноутбуки) — Понимание статистики, умение обосновать выбор модели и метрики Плюсом будет: — Опыт в графовом ML: GNN, graph embeddings, link prediction, PyG/DGL/NetworkX — Опыт в смежных направлениях: сетевой анализ, антифрод, рекомендации, маршрутизация — Работа с пространственными данными: OSM, PostGIS, H3/geohash, дорожные графы

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.

О нас: Мы строим ведущую платформу классифайд объявлений Узбекистана. ML - ядро нашего продукта: поиск, ранжирование, модерация и рекомендации. Команда находится на активной стадии построения ML-инфраструктуры с нуля: мы выбираем технологии, формируем процессы и закладываем фундамент для ML-платформы следующего поколения. Чем предстоит заниматься: - Развивать качество поиска: LTR-модели (LambdaMART/LightGBM), разрабатывать и интегрировать векторный поиск, улучшение поискового пайплайна (работа с саджестером, catpred). - Строить ML-модели для Trust & Safety: обнаружение мошенничества, модерация объявлений, NER в мессенджерах, скоринг нарушений. - Развивать систему рекомендаций - item2item, user2item рекомендации. - Участвовать в проектировании и развёртывании ML-платформы (SageMaker, MLflow, Feast, Evidently, Airflow).

Задачи: — Проектировать и разрабатывать multi-agent системы. — Интегрировать LLM с внутренними инструментами и сервисами. — Разрабатывать backend-сервисы и API для agent workflows. — Реализовывать RAG pipelines и tool/function calling. — Создавать CLI-based agent workflows и инструменты автоматизации. — Разрабатывать Computer Vision pipelines: object detection, multi-object tracking, person re-identification. — Разрабатывать multi-camera multi-person tracking для сетей камер. — Работать с видеопотоками в реальном времени и оптимизировать GPU inference. Требования: — Опыт работы с Node.js ecosystem и Express.js. — Уверенное владение Python. — Опыт работы с LLM APIs и prompt engineering. — Опыт работы с RAG, tool calling или agent frameworks. — Знакомство с Docker, Linux и Git. — Опыт работы с PyTorch и OpenCV. — Опыт в object detection и multi-object tracking. — Опыт работы с YOLO, ByteTrack / BoT-SORT или аналогами. — Понимание person re-identification и vector search. — Опыт работы с Milvus / FAISS. — Опыт обработки видео с ffmpeg / GStreamer. — Работа с RTSP и hardware decoding. Будет плюсом: — Опыт в NLP и ML. — Опыт развёртывания open-source LLM. — Опыт оптимизации inference. — Опыт работы с Vision-Language Models. — TensorRT / ONNX Runtime. Условия: — Удалённый формат работы. — Заработная плата: 1.600–3.200 USD net в месяц.

Задачи: — Владеть проблемой, превращая цели в AI-решения в продакшене. — Строить и адаптировать большие языковые модели (LLM); дообучать или дистиллировать модели для повышения производительности. — Обучать собственные модели, включая сбор и оценку данных. — Принимать решения о разработке или покупке на основе чётких метрик и ограничений. — Выпускать функции в продакшен и мониторить их эффективность. Требования: — Опыт инженерной работы 5+ лет с сильными навыками программирования. — Практический опыт с LLM: retrieval, использование инструментов и контекстное проектирование. — Подтверждённый опыт обучения моделей на реальных производственных данных. — Владение Python, PyTorch и использованием GPU. — Английский язык на уровне Intermediate и выше. Желательно: — Опыт работы с инференсом в реальном времени, модерацией контента или рекомендательными системами. — Знакомство с Docker, Kubernetes и облачными платформами. Условия: — Конкурентная зарплата и опционные программы — все являются партнёрами. — Официальное трудоустройство на Кипре (поддержка визы для сотрудника и супруга). — Комфортный офис в Лимассоле с обеспечением питания и перекусов. — Медицинская страховка для сотрудника и семьи. — Возможности для профессионального роста, уроки греческого языка и командные мероприятия.

Location: Hong Kong Nex is on a mission to help families rediscover the joy of movement. Created by parents for parents, Nex combines technology and play to deliver fun, social, and interactive experiences powered by natural body motion, encouraging kids and adults to move more, play more, and have fun together. Nex Playground, the company’s award-winning active play system, is purpose-built to get families moving year-round, with safety and privacy as core considerations in its intentional design. It is certified kidSAFE+ COPPA compliant and built to support healthy, active play for all ages and abilities. Nex Playground features a growing library of 50+ experiences, including motion and dance games, fitness and educational experiences, and Nex Originals. Content includes collaborations with partners like Hasbro, Sesame Workshop, and NBCUniversal. Nex has been recognized by Fast Company’s Most Innovative Companies, TIME’s Best Inventions, and Parents’ Best Entertainment System for Families, and has earned Red Dot, IDEA, and Core77 international design awards. We encourage you to explore Have Fun and Is Motion Gaming Back? , as they offer a deeper look into our culture, values, and explain how our approach to motion gaming differs from previous generations. Nex employees may be expected to collaborate with colleagues across different time zones. Depending on the role and location, occasional travel may also be required for company events, external meetings, and other business-related activities. Important Notice: ⚠️ To protect yourself from recruitment scams, please note that Nex communicates through official company emails ending in @nex.inc . If you receive a suspicious email or message, please do not respond and report it immediately to us. Location: Hong Kong Type: Full Time Internship (June 1, 2027 - August 31, 2027) The Role The Core Technology team develops the technologies that allow Nex Playground to use cameras, microphones, and other sensors to understand player movement and interactions in real time. As our Software Engineer Intern (ML Research, Core Technology) , you will explore and develop machine learning methods that expand what Nex Playground can perceive and understand. You may work on computer vision, audio, sensor fusion, or other sensing problems, taking ideas from research hypotheses through experimentation, evaluation, optimization, and working prototypes. You will collaborate with ML researchers and software engineers to build datasets and experimental workflows, evaluate ideas rigorously, and demonstrate promising technologies under real-world and on-device constraints. You will receive hands-on mentorship while owning a meaningful research project that contributes to our future products and technical direction. What You Will Do Develop and evaluate ML models and algorithms for computational perception and interaction. Design rigorous experiments, improve datasets, compare approaches, and understand failure modes. Build training and evaluation tools, and optimize promising models for real-time, on-device inference. Collaborate with researchers and engineers to prototype technologies on Nex Playground and communicate your findings. Should Have Must be currently enrolled in an undergraduate or graduate degree program at an accredited college or university. Strong interest in machine learning and enthusiasm for learning new methods and tools. Solid mathematical fundamentals and comfort with quantitative reasoning. Strong analytical and problem-solving skills. Programming experience in any language. A curious, systematic approach to learning through experimentation. Strong communication and collaboration skills. Nice To Have Experience with Python or familiarity with PyTorch, TensorFlow, or another ML framework. Interested in computer vision, audio processing, sensor fusion, or multimodal learning. Interested in model optimization, on-device inference, GPUs, NPUs, or resource-constrained devices. Interested in data curation workflows, or dataset quality analysis. Enthusiastic about games, interactive physical experiences, and helping people be active and healthy. We Offer Competitive compensation package. Flexible working hours and vacation policy. Product-driven culture that treasures talents and individual growth. Front-row seat and hands-on experience with cutting edge technologies in the evolving gaming field Nex is located in San Jose, California, USA and Hong Kong . Learn more about us at https://www.nexplayground.com/about . We encourage applications even if you don’t meet more than 50% of the requirements — we believe that experience comes in many forms! Nex is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, gender identity, and sexual orientation), national origin, ancestry, age, physical or mental disability, medical condition, genetic information, marital status, military or veteran status, or any other characteristic protected by applicable law. This policy applies to all individuals at every stage of the employment relationship, including all current and prospective employees, and covers all employment decisions including recruitment, hiring, job assignment, promotion, compensation, benefits, training, discipline and termination. We are committed to providing a workplace free from discrimination and harassment and to fostering an inclusive environment for all employees.

Ищем стажера машинного обучения - ML Engineer с небольшим опытом работы или со студенческими проектами в данной отрасли. Требуется прислать резюме для отклика на вакансию.

Location: Remote Who we are Our mission is to bring clarity and control to the world's most complex codebases. AI is accelerating code creation, but the infrastructure to understand, oversee, and evolve that code hasn't kept pace. Sourcegraph gives engineering organizations full visibility across their systems, precise context for their agents, and the ability to execute coordinated code changes at scale. As agentic development becomes the dominant engineering paradigm, we provide the context layer teams need to take control of their codebase. With Code Search, Deep Search, MCP, and Agentic Batch Changes, we deliver on that mission today - giving engineering teams and their AI tools the cross-repo context to navigate massive codebases with confidence, and the ability to make changes across hundreds of repositories at once. Companies like Stripe, Reddit, and Leidos rely on Sourcegraph to ship faster and with higher quality. We're backed by a16z, Sequoia, and Redpoint, and proud to operate as a globally distributed team that values high agency, direct communication, and customer love. If you want to build the infrastructure that lets every engineering team - and every agent they deploy - operate on their codebase with confidence, join us. Hours & location 🌎 While we hire almost anywhere in the world, we have a preference for someone to reside in the following locations for this role. However, if you feel qualified, we welcome you to apply regardless of location. No matter what, working hours must overlap with EST for at least 20 hours/week. Preferred locations: USA Why this job is exciting Sourcegraph is at the forefront of building AI tools to solve the biggest problems in the software industry, problems that only get bigger as codebases grow and as more of the work is done by agents. The Code Understanding team owns the surfaces where that intelligence meets the developer: Deep Search , our agentic, multi-step answer engine across an enterprise's entire lineup of codebases, Query Assist , turning natural language queries into Sourcegraph query syntax, Smart Hovers , concisely summarizing symbols right where devs need it, guided diff review, and the APIs that both humans and AI agents rely on every day. Most of what makes those surfaces tick is agent engineering : a blend of software engineering, machine learning, and statistics. Agent engineering tells us which model to use and when, how to retrieve and pack context, how to measure answer quality, where to fine-tune or distill a smaller model to cut costs and latency, and how to expand a single LLM call into a reliable multi-step agent. As the staff ML and agent engineer on Code Understanding , you'll be the technical owner of it: setting the tam’s direction for models, evaluations, and agentic systems, making our products measurably better, faster, and cheaper, and raising the team's fluency in building with models. This is a staff-level role : we're hiring a technical leader, not just a strong individual contributor. The primary need is a production ML and evaluation authority who also builds production agent systems. You'll own the hardest, most ambiguous problems in this space, set standards others follow, and influence direction beyond your immediate team. You'll get the exhilarating chance to drive the vision on how we can provide the best code understanding experience on the market by combining our deterministic, large-scale systems and AI into experiences never seen before. Concretely, you'll own work like: Agentic systems. You'll design and harden the multi-step, tool-using agent loops behind current and new agentic experiences, turning research and experiments into reliable, observable, and affordable products at enterprise scale. Pragmatic use of evaluations. Crafting agentic products means needing to tell when a change actually helped, which is hard when agents keep changing and the product keeps shifting. You'll bring judgment about where evaluations earn their keep, when to use targeted smoke tests and metrics, and how to avoid noise dressed up as rigor, so we can move fast with confidence. Models: selection, upgrading, and training. You'll decide which models we run where, drive upgrades, and fine-tune our own when that's the right call. Retrieval and context engineering. You'll push on how we ground models in a customer's code - retrieval, ranking, context windows, citations - to make answers more accurate and verifiable. Cost and latency. Every surface has a per-user economic budget. You'll treat cost and latency as product features, and profile, distill, cache, and right-size models so we can ship ambitious features sustainably. You'll do this on a small, senior-leaning team that ships quickly, owns a lot of product surface, and has streamlined product management: engineers here talk to customers, frame the problem, and own it end-to-end. You'll have real agency over technical direction and a direct line to the impact of your work. 📅 Within one month, you will… Get the Code Understanding products and their model/agent pipelines running end-to-end locally, and land your first improvements to a model, prompt, retrieval path, or eval. Build a clear picture of where the AI engineering pain is, and the product surfaces most constrained by them. Get to know the team and our customers, and start forming your own opinions about where our agentic products should go next. Join the team's on-call support rotation. 📅 Within three months, you will… Own a meaningful agentic slice of the product end-to-end, driving it from problem-framing through rollout and measurement. Establish how the team ships model and prompt changes responsibly: the evals, dashboards, and guardrails that make quality and cost regressions visible before customers feel them. Begin up-leveling teammates in building with models by pairing with them, reviewing their code, and modeling good agent engineering instincts. 📅 Within six months, you will… Be the recognized technical authority for agent engineering and agentic systems on Code Understanding. You will be the person teammates, and increasingly the wider department, defer to on model, eval, and agent-design decisions. Have measurably moved the products: better answer quality, lower cost/latency, or new agentic capabilities that weren't feasible before. Be setting the direction of the team's roadmap where it intersects agents, bringing conviction, backed by evidence, about which bets are worth making, and pulling other engineers up to execute on them. About you You are a staff engineer and technical leader with hard-won skills across production machine learning, evaluation, and agent systems . This high-leverage role relies on your ability to make sound model and evaluation decisions for a fast-moving product, build the production systems around them, steer technical direction, and be a force multiplier for a talented, product-minded team. You're equally comfortable reasoning about an eval harness, a fine-tuning run, a retrieval pipeline, and the multi-step agent loop that ties them together, and you make everyone around you better at all of it. You operate at staff scope: you own the most ambiguous, highest-risk problems in your domain, go into whatever codebase a problem requires, set standards and patterns others adopt, and translate fluidly between engineering goals and business objectives. You influence direction beyond your immediate team. You lead through technical excellence and mentorship. You have personally owned a production model lifecycle. You have trained or fine-tuned at least one model and taken it from dataset construction through evaluation, production rollout, and monitoring. You can explain how you chose between training or fine-tuning and prompting or retrieval, how you chose baselines and metrics, what error analysis revealed, and how production evidence affected the next version. You build agents, fluently and opinionatedly. You've designed multi-step agentic systems and made them reliable, observable, and cost-bounded. You have a point of view on where agents shine and where deterministic code or human judgment is required. You have strong evaluation judgment. You build representative datasets, meaningful baselines, useful error taxonomies, and release criteria that connect offline measurements to production behavior. You know where rigorous evaluations earn their keep, where lightweight smoke tests or qualitative review are enough, and where a precise-looking metric is misleading. You treat cost and latency as product constraints. You make measured quality, latency, and cost tradeoffs and use the appropriate combination of model selection, prompting, retrieval, caching, distillation, and fine-tuning rather than reaching reflexively for a more complex model. You operate autonomously on ambiguous problems. Given a rough product idea, a few customer quotes, and a Slack thread, you come back with a plan, a prototype, milestones, and a point of view on tradeoffs, without it being pre-scoped. You own high-technical-risk projects end-to-end. You contribute beyond your domain. As a senior IC, you go into whatever part of the codebase a problem requires, recognize issues beyond your immediate area, and translate between engineering goals and business objectives. You up-level the people around you. You mentor by pairing on hard problems, providing substantive design and code reviews, and spreading agent engineering literacy across the team. You see investing in your teammates' growth as part of the job. You're customer and product-driven. You're comfortable on customer calls and in feedback threads, you turn raw signals into requirements, scopes, and milestones, and you push back when feedback would lead the product astray. You're pragmatic, not a perfectionist. You ship the smallest correct thing, prefer robust solutions over complicated ones, and keep a high-quality bar with simplicity. On the engineering fundamentals: You're a strong software engineer who can ship production services. You're comfortable across our stack - Go on the backend, TypeScript on the frontend, GraphQL, Postgres, Docker - or you're clearly able and eager to get there. You're fluent with agentic coding tools, and you understand and own every line it submits. You're comfortable in an async-first, multi-service, fast-paced remote environment. Nice-to-haves: You've shipped an LLM-powered or agentic developer-facing product you can speak about opinionatedly: what worked, what didn't, what you'd do differently. You've fine-tuned, distilled, or trained models to meet cost, latency, or quality targets in production. Experience with retrieval, ranking, embeddings, or search relevance. Experience working directly with enterprise customers and translating their needs into a product. Experience mentoring or up-leveling engineers, especially raising a team's agent engineering fluency. Level 📊 This job is an IC4. You can read more about our job leveling philosophy in our Handbook. Compensation 💸 We pay above-market salaries because we want to hire exceptional people who can focus on building great products, not worrying about paying bills. As an open and transparent company, our compensation philosophy and pay bands are visible to every Sourcegraph teammate, and we strive to make our approach equitable, explainable, and competitive. Your base salary is determined by the IC4 pay band for your location zone (1-4). Our pay bands are informed by market data and designed to ensure competitive compensation wherever you live. During the recruiting process, we'll discuss the range applicable to you based on job level, relevant skills, experience, qualifications, and location zone. 💰 The starting salary for the IC4 pay band in each zone is: Zone 2: $176,000 USD Zone 3: $132,000 USD Zone 4: $88,000 USD 📈 In addition to competitive cash compensation, we offer meaningful equity (because when Sourcegraph succeeds, we want you to succeed, too) and generous perks & benefits . Interview process Below is the interview process you can expect for this role (you can read more about the types of interviews in our Handbook). It may look like a lot of steps, but rest assured that we move quickly and the steps are designed to help you get the information needed to determine if we’re the right fit for you… Interviewing is a two-way street, after all! We expect the interview process to take 5.5 hours in total. 👋 Introduction Stage - we have initial conversations to get to know you better… [30m] Recruiter Screen [30m] Hiring Manager Screen 🧑‍💻 Team Interview Stage - we then delve into your experience in more depth and introduce you to members of the team, including cross-functional partners… [60m] Resume Deep Dive [60m] System Design [60m] Harden a Code Research Agent Interview [60m] Cross-functional team collaboration / Values 🎉 Final Interview Stage - we move you to our final round, where you gain a better understanding of our business and values holistically… [30] Leadership We check references and conduct your background check Please note - you are welcome to request additional conversations with anyone you would like to meet, but didn’t get to meet during the interview process. Learn more about us You can learn more about what it is like to work at Sourcegraph by reading our handbook . We are an ambitious team who are collectively working hard to build the most influential company in the world. You can read more about our culture , competitive compensation and benefits here. Sourcegraph is an equal opportunity workplace; we welcome people from all backgrounds. Sourcegraph participates in E-Verify for U.S. Employees.

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

Требуется ML-инженер в компанию SimbirSoft. Требуемые навыки: — Python

Responsibilities: • Design multi-agent systems and integrate LLMs with internal tools • Develop backend APIs, RAG pipelines, and tool/function calling • Build computer vision pipelines for detection, tracking, and re-identification • Optimize real-time video and GPU inference Requirements: • Node.js/Express.js and Python • LLM APIs, prompt engineering, RAG, tool calling, or agent frameworks • Docker, Linux, and Git • PyTorch and OpenCV • Object detection and multi-object tracking (YOLO, ByteTrack/BoT-SORT) • Person re-identification, vector search, and video streaming (RTSP)

Location: Palo Alto Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE This role focuses on building and operating the ML platform that powers large-scale training, evaluation, and batch inference at Mistral AI. You will develop the infrastructure that enables researchers and engineers to run distributed GPU workloads reliably across clusters, hardware types, and regions. You will work across the full ML lifecycle, from workload scheduling and capacity management to platform APIs, observability, and production operations. You will take ownership of critical systems and help turn complex infrastructure into reliable, self-service capabilities. WHAT YOU WILL DO - Build the ML Platform: Develop services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference. - Orchestrate GPU Workloads: Build systems for queueing, admission control, quotas, priorities, preemption, and topology-aware placement. - Manage Compute Capacity: Improve how heterogeneous GPU resources are provisioned, allocated, and utilized across clusters. - Enable Multi-Cluster Execution: Place workloads based on capacity, data locality, hardware requirements, and organizational priorities. - Improve Researcher Experience: Create self-service workflows that make distributed workloads easy to launch, observe, debug, and reproduce. - Optimize Performance: Improve GPU utilization, scheduling latency, workload startup time, throughput, and infrastructure efficiency. - Build for Reliability: Develop observability, failure recovery, capacity planning, and operational tooling for critical ML workloads. - Operate What You Build: Participate in on-call rotations and troubleshoot issues across applications, schedulers, networking, storage, and GPU infrastructure. WHAT WE'RE LOOKING FOR - Have 4+ years of experience in ML infrastructure, distributed systems, Kubernetes platform engineering, or a related field. - Are proficient in Python or Go and comfortable working with production-grade distributed systems. - Have strong Kubernetes knowledge, including controllers, operators, CRDs, scheduling, networking, storage, and resource management. - Understand technologies such as Kueue, Karpenter, Volcano, and Kyverno, and the problems they address in workload scheduling, provisioning, and policy enforcement. - Understand distributed ML workloads, including training, fine-tuning, evaluation, checkpointing, and batch inference. - Are familiar with GPU infrastructure and technologies such as PyTorch, CUDA, NCCL, and high-performance networking. - Understand concepts such as quotas, priorities, preemption, gang scheduling, topology awareness, and workload admission. - Can diagnose performance and reliability problems across software, orchestration, networking, storage, and hardware. - Care about developer experience and enjoy turning complex infrastructure into simple, reliable interfaces. - Thrive in an ambiguous, fast-moving environment shaped by frontier AI research. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: New York, NY or Remote About Us: EvolutionIQ’s mission is to deliver state of the art technology that helps insurance claims teams make claims handling more accurate, fair, and efficient, so that more people impacted by injury or illness can continue their lives with dignity and stability. We are currently experiencing massive growth and to accomplish our goals, we are hiring world-class talent who want to help build and scale internally, and transform the insurance space. Our team is our #1 priority, and we have been named one of Inc.’s Best Workplaces 3 years in a row and Built In’s Best Places to work in 2025 and 2026! About You: As an ambitious AI / ML Engineer, you will play a key role in advancing our industry-leading medical synthesis product. You take strong ownership of your work and have a proven track record of leading projects end-to-end, from ideation and requirements gathering through architecture, implementation, deployment, and iteration. You are comfortable operating with a high degree of autonomy, making thoughtful architectural and technical decisions, and helping guide the direction of AI/ML solutions across the product. Thriving in a fast-paced startup environment, you stay current with the latest AI and machine learning research. You’re passionate about applying hybrid approaches that combine large language models (LLMs), statistical machine learning techniques, and retrieval-augmented generation (RAG) with embeddings-based models to deliver better, more reliable outcomes for our users. In this Role You Will: Lead AI/ML projects end-to-end, taking ownership from initial ideation and requirements gathering through architecture, development, deployment, and ongoing optimization Make high-level architectural and technical decisions for AI/ML systems, balancing scalability, performance, reliability, maintainability, and business impact Design, build, and deploy AI-powered and LLM-driven features for our claim synthesis product, including robust extraction of key information from complex medical documents and human-in-the-loop summarization workflows Develop and implement hybrid machine learning solutions that leverage statistical models, LLMs, and embeddings-based retrieval techniques such as RAG to improve system accuracy, scalability, and robustness Write clean, scalable, and efficient code while optimizing the performance of existing AI/ML systems in production Collaborate closely with data labelers and subject matter experts (SMEs) to rigorously evaluate AI system outputs and continuously improve model performance Partner closely with Product, Engineering, and other cross-functional teams to gather requirements, define technical approaches, prioritize work, and rapidly iterate on feedback Provide technical leadership and guidance to other engineers, helping establish best practices and driving consistency across AI/ML solutions Break down complex, ambiguous problems into well-defined technical solutions and drive projects forward while balancing short-term delivery with long-term architectural considerations Translate cutting-edge AI/ML research and novel techniques into production-grade, reliable, and maintainable solutions that operate seamlessly in live customer environments Skills Requirements: 5-8+ years of experience writing performant Python code following modern best practices Minimum 1 year of experience building and deploying products powered by large language models (LLMs) in fast-paced, professional environments Proven experience leading technical projects and driving them from concept through production deployment Experience making architectural decisions for AI/ML systems and evaluating technical tradeoffs across scalability, reliability, performance, and maintainability Hands-on experience with statistical machine learning techniques as well as hybrid approaches combining LLMs, retrieval-augmented generation (RAG), and embeddings-based models, including vector search and similarity measures Proven ability to build and integrate API services within service-oriented or microservice architectures Strong skills in evaluating and interpreting LLM outputs and AI model predictions, with a focus on aligning model behavior with real-world business and user outcomes Expertise in prompt engineering and fine-tuning of large language models for domain-specific applications Strong communication and collaboration skills, with the ability to influence technical direction and work effectively with Product, Engineering, SMEs, and other stakeholders Bonus Points: Experience translating state-of-the-art AI/ML research into production code Comfortable collaborating with data labelers and subject matter experts to improve training data and evaluation processes Experience building agentic or autonomous AI systems in production Background working with multimodal data (e.g., images, audio) Experience mentoring engineers or providing technical leadership across projects or teams Work-life, Culture & Perks: Compensation: The base salary range is $200-235K, with flexibility depending on a candidate’s background and experience. An annual bonus plan and company equity plan (RSUs) are also included in our compensation package. Well-Being: Medical, dental, vision, short & long-term disability, life insurance and AD&D, and 401k matching. Additional family, wellness, and pet benefits. Home & Family: Paid time off and sick leave, 100% paid parental leave (16 weeks for primary caregivers and 12 weeks for secondary caregivers). We offer a flexible schedule for new parents returning to work. Office Life: Catered lunches, happy hours, pet-friendly spaces, and monthly technology stipend. Growth & Training: $1,000/year for each employee for professional development, as well opportunities for tuition reimbursement. Sponsorship: We are open to sponsoring candidates currently in the U.S. who need to transfer their active visa. Please check with our Recruiting team if your visa is applicable for transfer. EvolutionIQ appreciates your interest in our company as a place of employment. EvolutionIQ is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Задачи: - Поддерживать текущие алгоритмические решения - Проектировать и внедрять AI-агентов (LLM, GPT-like модели) в различные процессы - Адаптировать и дообучать LLM (например, LLaMA, Mistral, YaLM 2) под специфику продукта - Разрабатывать RAG-пайплайн для поиска и обработки данных - Настраивать векторные БД (Pinecone/Weaviate/Qdrant) и оптимизировать кэширование Требования: - Знание современных AI-фреймворков и библиотек (LangChain, LlamaIndex и других) - Уверенное владение Python и PyTorch/TensorFlow - Практический опыт работы с API, Docker/Kubernetes и базами данных - Опыт внедрения RAG