Вакансии

Задачи: - Развивать инструменты хостинга ML-моделей: внедрять и развивать inference-движки и сопутствующую инфраструктуру, следить за мировыми трендами, делать R&D мировых решений. - Развивать ML-инфраструктуру: трекер экспериментов Clear ML, внутренние инструменты для разметки данных, векторную БД Milvus, решение для запуска распределенных вычислений, а также те инструменты и сервисы, про которые мы уже думали, но еще не внедрили. - Развивать внутренние сервисы: поддерживать планировщик вычислительных ресурсов для запуска DS-решений, бота для глубокого анализа каналов поддержки Центра ИИ и прикладные решения для управления кластерами и биллингом. Требования: - Опыт работы с Docker и Kubernetes для развертывания ML-моделей. - Опыт работы с Ansible. - Знание и опыт внедрения DevOps-практик: CI/CD, IaC, SRE. - Навыки работы с системами мониторинга Prometheus, Grafana, ELK. - Опыт программирования на Python на уровне понимания экосистемы, базовых библиотек и навыков написания прикладных программ. - Опыт проектирования и внедрения инфраструктурных решений. - Способность и готовность работать в условиях высокой неопределенности. Будут плюсами: - Опыт работы в роли MLOps-инженера или аналогичной должности. - Понимание жизненного цикла ML-решений и специфики их развертывания на production. - Опыт работы с векторными БД. - Знание ML-инструментов и библиотек. - Опыт работы с GPU-серверами и диагностики проблем с GPU. Условия: - Уровень дохода, который зависит от ваших технических знаний и навыков. Регулярно пересматриваем зарплаты с учетом профессионального роста. Финансовые бонусы тоже есть. - Заботу о вашем комфорте. Можете работать удаленно, гибридно или в офисе, в любом случае предоставим технику и все необходимое. Если вам по душе работать из офиса — обеспечим удобным местом, зонами отдыха, кухней с полезными перекусами. - Сильное инженерное комьюнити. Регулярно проводим внутренние технические конференции, обмениваемся опытом между командами. Вы всегда найдете, с кем посоветоваться. - Возможность сменить продукт, команду или предметную область внутри компании. Поддерживаем развитие в любом направлении. - Общение на «ты» и максимум горизонтальных связей в коллективе, чтобы быстрее договариваться и решать рабочие задачи. - Возможность делиться экспертизой. Помогаем выступать на конференциях, участвовать в подкастах или видео, писать статьи. - Поддержку профессионального развития. Организуем внутреннее и внешнее обучение.

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . As a Senior Staff Machine Learning Engineer, Feed Relevance , you'll drive the development of GenAI-powered recommendations, leveraging Reddit’s unique community landscape, rich conversational data, and dynamic engagement patterns. Your deep modeling expertise will directly shape the core architectures behind feed ranking, enabling breakthrough personalization for tens of millions of users. Responsibilities: Deliver on technical initiatives that have significant company-wide impact Set technical direction for the broader Relevance and Feeds teams at Reddit, able to identify opportunities and influence strategy across multiple orgs Work with management on goal setting, planning, and de-risking critical projects Mentor and grow Senior and Staff engineers Create a strong healthy engineering culture Qualifications: 10+ years of industry experience building systems for relevance driven products Subject matter expert in relevance, recommendation, and ML systems; able to solve complex problems in these domains that few others can Deep understanding of how to build sustainable software systems at a large scale engineering organization Experience in influencing organizations on technical direction/best practices Experience working with cross-functional teams such as design, product, business & data teams to deliver great experiences. Strong focus on user experience, usability, scalability, reliability and quality. You are an undying advocate for the user, and you have a deep intuition for how people & machines interact with software at scale. High empathy, excellent communication skills, and the ability to find compromise working across the entire engineering org. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-remote, #LI-JS5 Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $266,000 — $372,400 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Manager, Product Applied Science to build and lead a team at the core of Garner's product. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is a hands-on management role. You are responsible for the overall technical direction of your squad, building alongside your team of Applied Scientists to solve some of the hardest problems at the company. This is not a dashboards or descriptive-analytics team. Your team owns production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is leading a research pod at a top hedge fund. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Lead a team of 4-6 — hire, coach, and develop them, hold the bar on both rigor and speed, and help raise that bar across Applied Science Own the Population Health team and their outputs as a core responsibility Own your team's roadmap and delivery: what gets built, in what order, and whether it ships Set technical direction, and get in the weeds to do it — stay close enough to the code and the data to call the approach yourself, whether that's machine learning, optimization, a heuristic, or a simple rule, and set the standard for how your team makes that call Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Put your own hands on the problems where your judgment matters most — the highest-stakes, least-defined work — and deliver the algorithmic step-changes that move metrics like total-cost-of-care savings, steerage, and member engagement Turn ambiguous business goals into a clear problem set alongside Research, Product, Engineering, and business stakeholders, and represent your team's work to senior leadership Set the quality bar: review your team's work with rigor, and define the metrics that decide whether a solution is working Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred Deep technical credibility, with the range and judgment to lead scientists who are experts in their own right, and the willingness to stay hands-on Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them A bias toward action, quickly translating ideas into working prototypes to test approaches Strong communication skills, including at the executive level, with a track record of driving alignment across teams and functions A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to build and lead the team behind Garner's algorithmic systems — shaping how members find better care, and how applied science gets done at Garner, while staying close to the work yourself. What you'll get here: You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Compensation Transparency: The target base comp range for this position is $300,000-$390,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Staff Applied Scientist to join our Applied Science team. Garner is hiring Applied Scientists to design and ship the algorithmic systems at the core of our product. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is not a dashboards or descriptive-analytics role. You will own production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is a quantitative researcher at a top hedge fund. This is a player-coach role. You will be a hands-on-keys Applied Scientist, while also leading a small team that helps you deliver on your roadmap. Your time will be split between your own technical work and working with your team to shape how they approach their problems. This role is a good fit for someone who wants to develop their management toolkit while continuing to work closely on their own technical work, and it can lead either towards management or a deeper senior IC track. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Own the most ambiguous, high-stakes problems facing the company end-to-end, and set how the team frames and approaches them Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for, and set the standard for how the team selects and applies these approaches Deliver algorithmic breakthroughs that move the company's most important metrics, pioneering approaches that become how applied science is done at Garner Lead a small team — set their technical direction, unblock them when they're stuck, and share accountability for their growth and the quality of what they ship Review Applied Science work at the highest level across the company, ensuring the methods used across teams are sound and correctly applied Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred A bias toward action, quickly translating ideas into working prototypes to test approaches Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them Recognized technical authority, with the judgment to ensure the techniques used across an organization are sound Strong interest in mentoring or technically leading other Scientists — formal management experience is welcome, but not required Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong communication skills, including at the executive level, with a track record of driving alignment across an organization A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you’ll get here You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to work on high-impact problems in healthcare — shaping how members find better care through algorithmic systems that directly influence healthcare outcomes, and helping the scientists around you do the same. Compensation Transparency: The target base comp range for this position is $300,000-$390,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения.

Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения

Задачи: — разрабатывать и внедрять современные решения в области компьютерного зрения Требования: — опыт коммерческой разработки в области Computer Vision от 3 лет — хорошее понимание классического машинного обучения, статистического анализа, классических алгоритмов Computer Vision — практический опыт работы с нейросетевыми фреймворками PyTorch — умение читать научные статьи, воспроизводить решения и адаптировать их под реальные задачи — уверенное владение библиотеками NumPy, scikit-learn, Matplotlib, OpenCV — уверенная работа с Linux (предпочтительно Ubuntu, Mint или Debian) — опыт контейнеризации с использованием Docker и Docker Compose — уверенное владение Git — английский язык — Upper-Intermediate (B2) или выше (обязательно) — наличие проектов, которые были самостоятельно доведены до production Будет преимуществом: — опыт разработки систем видеоаналитики и/или видеонаблюдения — опыт оптимизации скорости инференса моделей с использованием ONNX Runtime и TensorRT — практический опыт работы с Graph Neural Networks (GraphNN/GNN) и Metric Learning — опыт работы с Kubernetes (K8s) Мы ищем человека, который: — любит решать сложные инженерные задачи — следит за современными исследованиями в области AI и Computer Vision — умеет самостоятельно принимать технические решения и доводить проекты до результата — готов работать над задачами полного цикла — от исследования и прототипирования до внедрения в production

Задачи: — разработка backend-сервисы и API на Python — создание AI-агентов и AI-пайплайны с использованием LangChain, LangGraph или аналогичных инструментов — работа с LLM: промптами, structured output, tool calling, контекстом и памятью — участие в создании RAG-систем: загрузке документов, поиске информации и формировании ответов на основе источников — интеграция решения с базами данных, внутренними API и внешними сервисами — работа с очередями задач, кэшированием, логированием и мониторингом — участие в проектировании веб-приложений: продумывать взаимодействие backend, frontend, баз данных, фоновых задач и внешних сервисов — интеграция backend с пользовательскими интерфейсами и при необходимости внесение базовых изменений во frontend — написание тестов, исправление ошибок и улучшение качества существующего кода — участие в обсуждении архитектурных и технических решений — быстрое создание прототипов, проверка гипотез и доработка решений по обратной связи пользователей. Требования: — коммерческий опыт разработки на python (fastapi, django или flask) и уверенное проектирование rest api — sql и реляционные бд (postgresql), git, docker; понимание клиент-серверной архитектуры и http-стека (авторизация, аутентификация, обработка ошибок) — базовая архитектура приложений: модульность, разделение ответственности, слои приложения, поддерживаемость кода и взаимодействие сервисов — синхронная и асинхронная обработка, фоновые задачи, очереди сообщений и брокеры (например, celery с rabbitmq/kafka) — практический опыт в ai/llm: разработка агентов и чат-ботов, использование langchain/langgraph, управление контекстом llm, оценка качества и защита данных — rag-системы, векторные базы данных, работа с полуструктурированными данными (pdf, таблицы) через pandas/numpy — кэширование, логирование и мониторинг (redis, elk/prometheus); базовое знакомство с ci/cd и развёртыванием — websocket и потоковая передача ответов от llm; базовое понимание frontend (html/css/js, react/vue/next.js) для интеграции интерфейса с api — использование современных ai-инструментов для ускорения написания кода, тестирования и подготовки документации Условия: — офисный формат работы: г. Москва — ежегодный пересмотр зарплаты и годовая премия — более 400 образовательных программ в СберУниверситете для развития — спортзал и зоны отдыха — расширенный ДМС, льготное страхование для семьи — 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: - Разрабатывать REST API с использованием FastAPI; - Проектировать и реализовать backend-сервисы с нуля; - Проводить интеграции с базами данных и системами обмена данными; - Обеспечивать масштабируемость и отказоустойчивость сервисов; - Писать и поддерживать автоматизированные тесты; - Настраивать и поддерживать CI/CD процессы для непрерывной интеграции; - Участвовать в планировании задач и работе по методологии Scrum; - Тесно взаимодействовать с командой для оперативного решения технических вопросов. Требования: - Сильные инженерные навыки, самостоятельность, понимание полного цикла доставки приложения; - Уверенный опыт настройки и использования CI/CD, работы с Docker и Kubernetes; - Понимание DevOps-культуры, умение написать и поддержать Helm-чарт/Dockerfile, разобраться с деплоем, настроить базовый мониторинг и алертинг; - Глубокие знания Python и опыт работы с FastAPI; - Опыт работы с PostgreSQL, ClickHouse, Kafka, Redis, Spark; - Умение писать чистый, поддерживаемый код с соблюдением best practices и знание архитектурных паттернов; - Опыт работы в высоконагруженных системах и решения реальных задач на production; - Аналитический склад ума, системное мышление и готовность самостоятельно доводить задачи до конца; - Продвинутое знание инструментов мониторинга (Prometheus, Grafana, Opensearch/ELK); - Глубокий опыт в DevOps, опыт работы с Triton Inference Server и другими инструментами деплоя ML-моделей будет плюсом.

Задачи: — Проектировать и развивать модели компьютерного зрения и видеоаналитики (детекция, трекинг, распознавание лиц, анализ поведения); — Интегрировать ML-модели в production (backend, edge); — Обучать и дообучать модели (fine-tuning, transfer learning); — Работать над реальными AI-проектами с бизнес-применением; — Сопровождать полный цикл разработки: от исследования до деплоя; — Оптимизировать модели и повышать качество CV-решений. Требования: — Уверенные знания Computer Vision и современных DL-подходов; — Глубокое понимание машинного обучения и нейронных сетей (CNN, detection/segmentation); — Уверенное владение Python; — Опыт работы с OpenCV, NumPy, PyTorch и/или TensorFlow; — Опыт работы с Git; — Опыт оптимизации моделей (speed/accuracy trade-offs, inference); — Опыт решения задач детекции, трекинга и анализа поведения людей. Условия: — Формат работы: офис, Астана.

Location: Remote - EMEA Workplace: Remote ABOUT US Launched in 2019, Constructor is an AI-first e-commerce search and discovery platform that helps shoppers find the right products at the right time and enables leading global e-commerce brands to drive meaningful revenue and conversion gains. ABOUT THE ROLE The ML Infrastructure team builds and operates the shared backend services and platform capabilities that Constructor's ML and product teams rely on to develop, deploy, and run machine learning at scale - from model serving (including LLMs) to the specialized storages and data pipelines behind it. We are a service team by nature: our roadmap is shaped by the needs of product teams, and we succeed when they can move faster with less friction. We're looking for a Senior Backend Engineer who is comfortable with, and motivated by this dynamic, and who will take end-to-end ownership of core services. In the near and mid-term, our focus areas include: - Making ML workloads easy to run, scale, and operate for every team - Providing self-service, configurable infrastructure components to ML and product teams - Owning and evolving core ML services and related pipelines with a high bar for reliability and performance CHALLENGES YOU WILL TACKLE - Design, build, and operate high-load distributed backend services that power the company's ML infrastructure - Take end-to-end ownership of core ML services and related data pipelines: from design and implementation to deployment, observability, and continuous improvement - Partner with ML and product teams to understand their needs and turn them into reliable, reusable platform capabilities - Make and defend technical decisions: evaluate trade-offs, design first, and own the outcome - Be responsible for what you and your team do REQUIREMENTS - 5+ years of professional experience in backend or platform engineering - Extensive Python knowledge (our primary language) - Hands-on experience developing on a public cloud (AWS, GCP, or Azure) or with self-managed Kubernetes - AWS is our primary platform, but we're not limited to it - Experience designing and building distributed, high-load services and APIs - Strong knowledge of data structures, algorithms, and their trade-offs - Design-driven engineering mindset: we're a coding-assistant-friendly environment and expect you to use modern AI tools - but you lead with system design and a strong understanding of their applicability and trade-offs - Ownership and proactivity: you can't close your eyes to problems, but are ready to solve them and take responsibility for outcomes - You are friendly and willing to help your teammates & others NICE TO HAVE: - Experience with Rust (or C/C++/Go) - a strong plus - Experience developing or contributing to ML platforms or ML infrastructure - a strong plus - Experience setting up and operating any vector database (Qdrant, Milvus, Weaviate, OpenSearch, pgvector, etc.) - a strong plus - Experience with model serving / inference infrastructure, including LLMs - Experience with Infrastructure as Code (Terraform or similar) BENEFITS - 🏝️ Unlimited vacation time - we strongly encourage all of our employees take at least 3 weeks per year - 🌎 Fully remote team - choose where you live - 🛋️ Work from home stipend! We want you to have the resources you need to set up your home office - 💻 Apple laptops provided for new employees - 🧑‍🎓 Training and development budget for every employee, refreshed each year - 👪 Maternity & Paternity leave for qualified employees - 🧠 Work with smart people who will help you grow and make a meaningful impact - 💵 Base salary: $80k–$120k USD, depending on knowledge, skills, experience, and interview results - 📈 Stock options - offered in addition to the base salary - 🎉 Regular team offsites to connect and collaborate DIVERSITY, EQUITY, AND INCLUSION AT CONSTRUCTOR At Constructor.io http://Constructor.io we are committed to cultivating a work environment that is diverse, equitable, and inclusive. As an equal opportunity employer, we welcome individuals of all backgrounds and provide equal opportunities to all applicants regardless of their education, diversity of opinion, race, color, religion, gender, gender expression, sexual orientation, national origin, genetics, disability, age, veteran status or affiliation in any other protected group. Studies have shown that women and people of color may be less likely to apply for jobs unless they meet every one of the qualifications listed. Our primary interest is in finding the best candidate for the job. We encourage you to apply even if you don’t meet all of our listed qualifications. How we handle your data. By submitting this application, you acknowledge that Constructor will collect and process the personal data you provide for the purpose of evaluating your candidacy for this role. If you are not selected, we will retain your data for a limited period as described in our Recruitment Privacy Notice https://constructor.com/recruitment-privacy-notice. After that period your data will be deleted or anonymized.

Location: Europe; United States Workplace: Remote ABOUT CANTINA: Cantina is a new social platform founded by Sean Parker with the most advanced AI character creator. Our bots are lifelike, social creatures that can interact wherever people are online—across voice, video, and text. Create yourself, imagine someone new, or choose from thousands of characters to share infinitely scalable, personalized content and seamless group chat. If you’re excited about how AI can shape creativity and social interaction, come help us build what’s next.   ABOUT THE ROLE: We’re looking for a Research / ML Engineer to join our Speech Team to build state-of-the-art speech systems end-to-end—from data specs through production inference. You’ll drive the model ↔ data ↔ eval flywheel for VC and adjacent tasks (controllable TTS, voice design and more), partnering closely with research, data, and infra to ship fast, reliable, and cost-aware models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You will thrive in this role if you: - See research and engineering as two sides of the same coin and enjoy owning work end-to-end. - Are results-oriented, flexible, and willing to pick up whatever moves the needle. - Like collaborating closely with infra, data, and product to ship measurable improvements. - Enjoy designing experiments, listening tests, and metrics that correlate with user-perceived quality. - Eager to learn every-day, find and solve unique large-scale problems. WHAT YOU’LL DO: - Model Building: Architect, implement, pre-train, fine-tune, and post-train/alignment (e.g., GRPO/DPO) for large-scale speech models. - Experimental Design: Design, run, and analyze scientific experiments to advance our understanding of the models. - Tool Development: Develop and improve dev tooling to enhance team productivity. - Full-Stack Contribution: Contribute to the entire stack, from low-level optimizations to high-level model design. - Data Ownership: Define data requirements and collaborate on acquisition, curation, augmentation, labeling quality, and synthetic data strategies. - Rigorous Evaluation: Design automated objective/subjective evaluations—listening tests, SV/WER/ASR-based metrics, robustness & bias checks, and red-team studies. - Pipeline Delivery: Harden the training → evaluation → inference pipeline; profile latency, memory, and cost; and meet production SLAs with robust monitoring and rollback. - Safety & Responsibility: Contribute to safety/consent guardrails and to misuse/abuse mitigation for responsible speech technology.   WHAT YOU’LL BRING: - Exceptional research/development experience with large-scale audio models (>8B parameters, >500k hours of data). - Deep hands-on experience with diffusion and/or flow-matching transformers, including practical knowledge of samplers, schedules, conditioning mechanisms, and distillation. - Deep hands-on experience training audio VAEs, neural audio codecs, and vocoders latent/tokenizer design, reconstruction and perceptual objectives, adversarial training. - Strong experience with multi-node, multi-GPU distributed training (FSDP/DeepSpeed or equivalent). - Strong software engineering skills with a proven track record of building complex systems. - Strong with PyTorch and performance work (profiling, CUDA/Triton/C++ as needed) and writing reliable production-quality code. - Shipped large-scale speech/audio or multimodal generative models to production. - Background in working with large-scale ML data, and the ability to iterate on data and triangulate quality using both subjective and objective signals. - Experience with voice cloning, speech control/steerability, or expressive speech generation. - Notable publications and/or open-source contributions in speech/audio/ML. COMPENSATION: When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.   BENEFITS FOR U.S.-BASED ROLES: - Competitive salary and generous company equity - Medical, dental, and vision insurance – 99.99% of premiums covered by Cantina - 42 days of paid time off, including: - 15 PTO days - 10 sick days - 15 company holidays - 2 floating holidays - Generous parental leave & fertility support - 401(k) retirement savings plan - Lifestyle spending account – $500/month to use however you’d like - Complimentary lunch and snacks for in-office employees - One Medical membership, and more!

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 end-to-end execution, training, and data infrastructure that powers Mistral’s agentic models and coding assistants. You will be a core contributor to our agent research stack: designing scalable systems for synthetic data generation, building ultra-fast training and RL execution environments, and maintaining high-throughput execution engines. You will tackle the engineering challenges at every step of the agent lifecycle: from orchestrating 1M+ concurrent and short-lived sandboxes for untrusted code execution to optimizing agent training codebases, distributed trajectory collection pipelines, and dataset processing workflows across massive hybrid and multi-cloud clusters. WHAT YOU WILL DO - Large-Scale Sandboxing Infrastructure: Design, deploy, and operate our high-throughput sandboxing platform, executing LLM-generated untrusted code across over 1 million isolated environments concurrently for model evaluation and interactive RL environments. - Agent Data Generation Pipelines: Architect and scale high-throughput pipelines for synthetic code generation, agent trajectories, rollouts, and self-play data collection to power post-training and RL loops. - Training Codebase & Systems Optimization: Optimize agent training codebases and distributed execution runtimes (PyTorch, Ray, SLURM/Kubernetes) to minimize multi-step rollout overhead, improve GPU utilization, and eliminate scaling bottlenecks. - Low-Latency Orchestration & Warm Pooling: Reduce sandbox cold-start times to sub-second levels using container warm pools, snapshot/restore technology (e.g., CRIU, microVMs), and optimized image delivery layers across hybrid clusters. - Multi-Cluster Queueing & Resource Allocation: Implement Kubernetes-native custom controllers, CRDs, and queuing systems to dynamically route short-lived evaluation, synthetic data, and agent execution tasks across diverse hardware fleets. - Isolation, Security & Security Boundary: Ensure strict multi-tenant network and process isolation for untrusted agent code using container/sandboxing runtimes (e.g., gVisor, Firecracker) and default-deny network postures. - Operational Excellence: Maintain high availability, telemetry, and automated self-healing across millions of transient jobs while participating in on-call rotations for critical agent training and execution pipelines. WHAT WE'RE LOOKING FOR - 4+ years of experience in Systems Engineering, Distributed Systems, Cloud Infrastructure, or MLOps supporting LLM/RL workloads. - Data & Pipeline Engineering: Proven experience building high-throughput data processing and generation pipelines for large-scale datasets (e.g., Ray, Spark, custom distributed queues). - Deep experience with Kubernetes & Container Tech: Strong expertise writing custom K8s operators/controllers, managing Linux cgroups/namespaces, and optimizing Docker image layers and distribution systems. - High-Performance Software Engineering: Advanced proficiency in Python, Go, C++ or Rust, with a track record of profiling and optimizing high-performance ML or backend systems codebases. - Sandboxing & Isolation Technologies: Hands-on experience with lightweight virtualization, container runtimes, or WASM (e.g., Docker, gVisor, Firecracker). - Queueing & Scheduling: Deep familiarity with task queue systems, resource schedulers, and low-latency queuing architectures for high-volume, short-lived workloads. - Comfort with Ambiguity: Passion for working directly alongside AI researchers to rapidly turn frontier agent ideas into scalable, production-grade infrastructure. 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: Palo Alto; New York 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   Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. You will develop SOTA models across different modalities such as text, image, and speech. By developing novel methods and research ideas you will apply these models across a diverse set of use cases and domains. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions that turn the needle.   WHAT YOU WILL DO   • Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. You don’t panic when you see OOM errors or when NCCL feels like not wanting to talk. • Generate and curate data for pre-training and post-training, working on evaluations and making sure the model’s performance beats expectations. • Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment. • Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines. • Manage research projects and communications with client research teams.   WHAT WE'RE LOOKING FOR     • You have excellent communication skills. You are at ease explaining complex technical concepts to both technical and non-technical audiences. • You’re an expert with PyTorch or JAX. • You’re not afraid of contributing to a big codebase and can find yourself around independently with little guidance. • You write clean, readable, high-performance, fault-tolerant Python code. • You don’t need roadmaps: you just do. You don’t need a manager: you just ship. • Low-ego, collaborative and eager to learn. • You have a track record of success through personal projects, professional projects or in academia.   NICE TO HAVE     • Hold a PhD / master in a relevant field (e.g., Mathematics, Physics, Machine Learning, Computer Science & Engineering), but if you’re an exceptional candidate from a different background, you should apply. • Can bring a variety of research experience (agents, multi-modality, robotics, diffusion, time-series). • Have contributed to a large codebase used by many (open source or in the industry). • Have a track record of publications in top academic journals or conferences. • Love improving existing code by fixing typing issues, adding tests and improving CI pipelines.     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: Amsterdam, Netherlands; Belgrade, Serbia; Berlin, Germany; Limassol, Cyprus; London, United Kingdom; Madrid, Spain; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Remote, Germany; Warsaw, Poland; Yerevan, Armenia Are you an engineer who loves diving deep into ML models, building standalone CLI tools, and exploring how far AI agents can go in autonomous coding? Do you want to rethink how software is built and use the tools you create every single day? About JetBrains We create intelligent software development tools for developers and teams. More than 15 million users, over 300,000 companies, and 88 of the Fortune Global Top 100 rely on our products to solve real, complex problems. Our mission is simple: make development teams more productive and AI adoptable at scale. What you’ll do We build Junie, our AI coding agent, available across JetBrains IDEs and as a standalone CLI. We're combining the latest advancements in AI and LLMs with our decades of software development expertise. We are working in three main areas: growing Junie CLI into a general-purpose agent independent of any IDE, optimizing model ensembling and setups for the best cost-to-intelligence ratio without compromising code quality, and pushing next-level autonomy so Junie can own, maintain, and evolve entire subsystems over multi-day horizons. You’ll have end-to-end ownership over these initiatives, taking work from early research to production code while shaping the future of autonomous development. Day to day, you will: Develop and ship new features for Junie across CLI and model integrations. Experiment with model setups, ensembling, and cost efficiency strategies. Build and test autonomous agent behaviors for long-term project maintenance. Own the full development cycle from initial research to production-ready code. Use Junie daily to continuously iterate on its quality and user experience. What you’ll bring Comfort taking full ownership of decisions and implementation. The curiosity to challenge assumptions and experiment with new ideas. The ability to learn new concepts quickly and adapt quickly. A mindset focused on long-term impact rather than short-term workarounds. Strong interest in autonomous AI systems and software engineering tools. What you’ll need A proven track record of developing and shipping machine learning or full-stack software products. Hands-on experience working with LLMs, prompt engineering, or agentic workflows. The ability to write high-quality production code across the full development lifecycle. Practical experience building or fine-tuning ML models. What success looks like In this role, success means taking bold experiments in agent autonomy and cost efficiency and turning them into reliable, production-ready capabilities. You'll know you're succeeding when Junie can tackle complex multi-day engineering tasks autonomously and the tool you build every day actively improves your own engineering workflow. Why join JetBrains? Strong base salary. We offer competitive pay that reflects your skills and experience. Flexible work location. Enjoy the freedom to work from home or from the office. Remote work. Spend up to 30 days per year working remotely from abroad. Extra time off. More days to relax, recharge, and do the things you love. Medical insurance allowance. Enjoy peace of mind for you and your family Learning and development opportunities. Access to conferences, courses, and language classes. Relocation support. We help make your move as smooth and stress-free as possible. Language classes. Pick up the local language or sharpen your English skills. Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays. Mental health support. To help you feel your best, we provide easy access to professional mental health services. Sports benefit. Enjoy an on-site gym or sports club stipend. Internal events. Join company-wide celebrations and team gatherings. *Some benefits may vary depending on location. #LI-DNI We are an equal opportunity employer We know great ideas can come from anyone, anywhere. That’s why we do our best to create an open and inclusive workplace – one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation. We process the data provided in your job application in accordance with the Recruitment Privacy Policy.

Location: United Kingdom, London Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Role Description: We are looking for an independent, dedicated Staff Machine Learning Engineer to join the Visual Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D. VCG’s mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency. What you’ll be doing: Develop and improve core generative models for graphics and video processing for a variety of applications, such as live video streaming, game rendering, etc. Research and prototyping of new methods for graphics or video enhancement, and data generation pipelines from generative models Collaborate with team members on drafting publications and patent submissions Participate and assist the team in core R&D work with other teams within Sony Interactive Entertainment What we’re looking for: PhD or MSc in Computer Science, Electronic Engineering, Artificial Intelligence, Machine Learning, Computer Graphics or a related field, or equivalent skills evidenced by work experience in the specific domain of the post 7+ years of experience in demonstrable research experience in industry or academia Strong background in: TensorFlow or PyTorch; Python and packages/libraries related to computer vision or graphics; evidenced by the development of advanced applications in image/video/graphics processing or computer vision (minimum 7 years), Evidence of leading technical developments by several publications in top-tier conferences like CVPR, ECCV, ICCV, SIGGRAPH or similar Solid background in one or more of the following: (i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design; (ii) design and test of advanced convolutional, recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner; (iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar Desired qualifications: Publications, e.g. in top-tier conferences and journals: IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, conferences like IEEE CVPR/ICCV/ECCV, NeurIPS, ICML, ICLR, or similar Some theoretical understanding of the graphics pipeline, including coordinate transformations between frames of reference, PBR materials, rasterization, lighting, and ray tracing Experience in image processing theory and methods, evidenced by the development of practical designs in this area Some experience with 3D engines such as Unreal or Unity; in particular, generation of datasets, extraction of G-buffers, motion vectors, etc. Benefits: Discretionary bonus opportunity Hybrid Working (within Flexmodes) Private Medical Insurance Dental Scheme 25 days holiday per year On Site Gym Subsidised Café Free soft drinks On site bar Access to cycle garage and showers Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Требования: — 5+ лет в production ML/NLP: системы, которые вы поддерживали после запуска — техническое лидерство: решения по стеку за команду, ревью, ответственность за сдачу шире своих задач — RAG целиком: гибридный поиск, чанкинг, реранкинг, методика оценки — self-hosted инференс открытых моделей, квантизация, расчет GPU — eval-наборы: чем вы докажете, что новая версия не хуже прошлой — развертывание в закрытом контуре, на инфраструктуре заказчика Половина роли техническая, половина переговорная: защищать архитектуру и метрики качества перед ИТ-директором заказчика. Условия: — Оплачиваемый испытательный срок — Бонусы за сдачу проектов — Офис в Алматы — Зарплата: 1 000 000 – 1 400 000 ₸