Вакансии

Location: Москва Workplace: Комбинированный Мы ищем ML-инженера, который будет развивать качество e-commerce рекламы, используя технологию full-stream атрибуции https://ads.vk.ru/help/general/ecomm/dyn_ret/fsa . Наша команда занимается end-to-end разработкой и поддержкой всего пайплайна рекомендаций от обработки данных до выкатки в продакшен и проведения А/B-тестов. Задачи Улучшение базового качества рекомендаций Развитие высоконагруженных сервисов рекомендаций Проведение и анализ A/B-тестов Требования Опыт работы ML-инженером от двух лет Знание алгоритмов машинного обучения и принципов построения рекомендательных систем Опыт промышленной разработки на Java Понимание принципов работы высоконагруженных систем Будет плюсом Опыт разработки рекомендательных систем и моделей ранжирования Опыт проведения и анализа A/B-тестов Опыт работы с YTsaurus, CatBoost, Spring и Java

Location: Belgium, Zaventem 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. Why PlayStation? 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 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. The Direct to Consumer (D2C) Data Science organization comprises Data Science, Data Engineering and ML Engineering practices. D2C Data Science helps PlayStation grow and operate its digital business across commerce, subscriptions, payments, lifecycle experiences and player-facing services. We partner with product, engineering, finance, marketing and operations teams to turn experimentation, forecasting, AI and production-quality measurement into better player experiences. Role overview: Within D2C Data Science, the D2C ML Engineering team is the center of excellence for machine learning engineering. We build and operate the in-house machine learning platform that data scientists use to design, deploy, monitor and experiment with the models powering production decisions across the D2C portfolio. This is a hands-on software engineering role supporting the production use of machine learning. You will build services, pipelines and tools that deliver solutions across payments, fraud prevention and other D2C use cases, including bringing machine learning capabilities into production. You will take ownership of defined components or improvements within our existing architecture, working closely with data scientists and other ML engineers across a globally distributed team. Strong software engineering foundations and a practical understanding of machine learning will help you grow into the wider ML lifecycle. This role follows a hybrid working arrangement, with work primarily performed remotely and attendance at the Brussels office as needed. What you'll be doing: Contribute to services and pipelines that support activities such as model training, batch or real-time scoring, and production monitoring. Develop and maintain data transformations, feature aggregations and integrations using Python or Java and SQL. Help integrate model deployments and A/B experiments in partnership with data scientists and other ML engineers. Improve automated testing, deployment, monitoring and troubleshooting, and participate in the team’s on-call rotation (no nights). What we're looking for: Master’s degree in Computer Science, Machine Learning or a related field, with 3+ years of relevant experience. Strong software engineering foundations, with experience building, deploying and supporting production systems using Python or Java, including monitoring and troubleshooting. Ability to use AI-assisted development tools effectively, assess their output, and validate changes through appropriate testing and monitoring. Practical understanding of machine learning concepts and workflows, with some experience applying or supporting ML/AI systems and using SQL to prepare and investigate data. Ability to work closely with data scientists and product partners across a globally distributed team, and translate their needs into reliable software. Desirable skills: Exposure to fraud, payments, risk, account security or other adversarial e-commerce problem spaces. Interest or experience in generative AI applications and agentic workflows. 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.

Location: Москва, RU; Россия О команде Наша команда развивает LLM Gateway — единую точку доступа к языковым моделям в Авито. Мы помогаем продуктовым командам быстро и безопасно получать доступ к подходящим моделям, управлять лимитами, видеть стоимость каждого запроса и отслеживать поведение моделей в продакшене. Какие задачи вас ждут: развивать LLM Gateway для Авито, чтобы создавать продукты с LLM было быстро и удобно; автоматизировать выдачу и настройку доступов к моделям; развивать управление потреблением LLM: рассчитывать стоимость LLM-вызова, настраивать бюджетные лимиты для клиентов и приоритеты; развивать динамическую балансировку трафика между LLM-моделями; определять SLA платформы и методику его расчёта, настраивать мониторинг и алёрты, поддерживать SLA; интегрироваться с платформой инференса, автоматизировать развёртывание моделей и открывать к ним доступ для пользователей в инфраструктуре Авито; тесно взаимодействовать с DS-инженерами, погружаться в работу LLM, следить за развитием индустрии и внедрять новые технологии. Мы ждём, что вы: имеете опыт технического лидерства в крупных направлениях с высокой степенью неопределённости: собирали требования, планировали работы, декомпозировали задачи, отвечали за реализацию и отслеживали результаты; имеете опыт поддержки критической высоконагруженной инфраструктуры: настраивали мониторинг и алёрты, участвовали в дежурствах 24/7; программировали на Python или Go от 5 лет; имеете опыт в MLOps или LLMOps: разворачивали и поддерживали инфраструктуру для автоматизации работы с ML- и LLM-моделями; понимаете основы работы LLM: чем prefill отличается от decode, зачем нужен KV-cache, что такое TTFT; умеете работать с задачами высокой неопределённости и выстраивать коммуникацию с командами. Будет здорово, если вы: работали с LLM API и LLM Gateway, например LiteLLM; создавали внутренние платформенные решения; работали с системами мониторинга LLM, например Langfuse, Opik и другими; создавали и поддерживали админки для внутренних пользователей; работали с Kubernetes и GPU; работали с DWH, например ClickHouse и Trino. Работа у нас — это: расширенная забота о здоровье: ДМС, страхование жизни, онкострахование; сервис психологической поддержки, а также финансовые, юридические и ЗОЖ-консультации для вас и ваших близких; корпоративный спорт: групповые тренировки, велбиинг-приложение; спецпредложения от партнёров и скидки на сервисы Авито: бронирование жилья, услуги продвижения, ипотечное страхование; компенсация питания.

• Опыт в DevOps / Cloud от 5 лет, основная платформа — AWS. • Практический опыт развёртывания ML-моделей или Python-сервисов на AWS, включая SageMaker.

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. https://job-boards.greenhouse.io/anthropic/jobs/4669581008 About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Team Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. About the role As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity. Responsibilities: Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI. Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows Collaborate to translate experimental requirements into production-ready infrastructure Develop large scale data pipelines to handle advanced language model training requirements Optimize large scale training and inference pipelines for stable and efficient reinforcement learning You may be a good fit if you: Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems Are a strong communicator and enjoy working collaboratively Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale Have proven track record of building large-scale data pipelines and distributed storage systems Excel at diagnosing and resolving complex infrastructure challenges in production environments Can work effectively across the full ML stack from data pipelines to performance optimization Have experience collaborating with other researchers to scale experimental ideas Thrive in fast-paced environments and can rapidly iterate from experimentation to production Strong candidates may also have: Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.) Background in building infrastructure for AI research labs or large-scale ML organizations Knowledge of GPU/TPU architectures and  language model inference optimization Experience with cloud platforms (AWS, GCP) at enterprise scale Familiarity with VM and container orchestration. Experience with workflow orchestration tools and experiment management systems History working with large scale reinforcement learning Comfort with large scale data pipelines (Beam, Spark, Dask, …) The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Москва Привет! Это GigaChat Vision — команда, которая делает полный цикл обучения VLM моделей, включающий в себя стадии pre-training, SFT и DPO/RL. Мы ищем сильных инженеров и исследователей, которые будут двигать вперед качество мультимодального GigaChat. _Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram. Диалог займёт примерно 10 минут. Его задача — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным!_ Обязанности * Самостоятельная работа в рамках заданного направления развития качества VLM: от планирования, до выкатки в прод; * Планирование и работа над исследовательскими задачами по архитектуре модели, стратегии и данным обучения; * Формирование гипотез по улучшению качества модели, самостоятельное планирование экспериментов по их подтверждению и постановка multi-node обучений на большом количестве GPU (тысячи A100/H100); * Решение сложных инженерных задач, развитие и оптимизация кодовой базы обучения и эвала VLM, написание чистого кода и проведение code review; * Анализ точек роста и определение способов их закрытия. Требования * Сильный технический бэкграунд в modern ML: понимание того, как данные, архитектура и стратегии обучения влияют на итоговое качество модели; * Опыт полного цикла обучения VLM или LLM; * Глубокие знания или опыт работы в области computer vision; * Понимание принципов работы распределенного обучения (FSDP) или опыт обучения моделей на больших GPU кластерах; * Наличие навыков технического лидерства: проведение code-review, помощь и менторинг младших сотрудников; * Умение работать, принимать решения и выстраивать процессы в условиях неопределенности; * Умение и желание взаимодействовать с коллегами, вносить собственные идеи и предлагать пути достижения общих целей. Условия **Будет плюсом:** * Опыт полного цикла обучения VLM; * Наличие публикаций в области NLP или CV; * Опыт решения задач по оптимизации обучения; * Опыт участия в соревнованиях по машинному обучению или программированию.

Location: Москва Мы делаем Voice Mode в GigaChat — голосового AI-агента для сложных пользовательских сценариев. Хотим, чтобы общение с ним было максимально похоже на разговор с человеком: с естественной речью, длинным диалогом и нормальной реакцией на перебивания. Отдельно работаем над надежной работой с внешними инструментами, низкой задержкой и эмпатией. У нас уже есть все основные компоненты для работы: LLM GigaChat, harness для работы с инструментами и оркестрации, ASR-модель для русского языка GigaAM, audio-conditioned модель GigaChat Audio и LLM-based синтез речи GigaTTS. Попробовать голосовой режим наших продуктов можно на ⁠[giga.chat](https://giga.chat/). Обязанности * Разрабатывать архитектуру взаимодействия voice-слоя и agent harness: перебивания, barge-in, turn-taking, асинхронные скиллы * Экспериментировать с обучением LLM: от SFT до RL, адаптировать модели под конкретные домены и улучшать работу с функциями и скиллами * Строить систему оценки качества голосовых агентов: от WER, latency и GCR до ⁠[tau-like](https://taubench.com/) бенчмарков * Улучшать работу системы на реальном трафике: работать с длинными диалогами, ошибками распознавания, телефонным каналом, шумом, акцентами, перебиваниями и спонтанной речью В команде можно сфокусироваться на одном из направлений. Не обязательно одинаково глубоко разбираться в голосовых технологиях, agent harness и обучении моделей. Требования Мы ждем, что вы --------------- * Имеете практический опыт распределенного обучения больших моделей и понимаете, как оно устроено под капотом * Строили пайплайны для оценки качества и сбора данных для генеративных моделей * Умеете решать продуктовые ML-задачи полного цикла: от сбора разметки до внедрения в продакшн Будет плюсом, если вы --------------------- * Применяли RL-методы для обучения LLM на практике * Занимались обучением мультимодальных LLM * Работали над улучшением ASR, TTS или других audio-компонентов * Понимаете, как устроены сложные harness-системы, и писали нетривиальные скиллы для собственных разработок или реальных продуктов Условия * крупнейшее DS&AI community — более 600 DS-специалистов банка * дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира * возможность быть соавтором НИРов и статей для международных конференций * возможность выбрать удобный формат работы: гибрид или офис * ежегодный пересмотр зарплаты, 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 * ипотека выгоднее до 7% для каждого сотрудника * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Lithuania; Estonia; Czechia; Moldova; Ukraine; Spain RealAdvisor is one of the leading digital platforms in the European real estate ecosystem. Founded in Switzerland, we now support 5,000+ real estate agencies and attract 10+ million users per year across Europe. We are scaling fast across multiple markets with a clear mission: help real estate professionals grow through data, transparency and smart technology. At RealAdvisor, we value ownership, autonomy and impact. Role Overview We are looking for a Machine Learning Engineer to take end-to-end ownership of our geospatial data products. This is not a model-tuning role. You will design, build, and operate the full data lifecycle behind our real-estate insights — from orchestrating complex pipelines on Kubernetes to delivering high-performance geospatial visualizations with Mapbox. This is a builder role. You should be as comfortable debugging an Airflow DAG as you are computing spatial statistics or optimizing analytical queries. You’ll have full autonomy over a modern, high-performance stack: Orchestration & Operations: Airflow, Kubernetes, Docker Data Storage: Postgres (PostGIS) for spatial workloads; ClickHouse for large-scale analytics Programming: Production-grade Python Requirements Key Responsibilities End-to-End Pipelines: Design and deploy automated pipelines (ETL) using Airflow to ingest diverse datasets (Real Estate transactions, listings, geo-data). Geospatial Analytics: Compute advanced spatial metrics (e.g., pricing honeycombs, average prices per neighborhood) using PostGIS and Python. Infrastructure Management: Maintain and optimize the Kubernetes environment where your pipelines and models run. Visualization Engineering: Transform your analytical outputs into interactive map layers (Vector Tiles) that integrate seamlessly with Mapbox on our frontend. What We’re Looking For Truly Full-Stack (Data): You don’t believe data scientists should stop at notebooks. You own infrastructure, deployment, and reliability because that’s how real value is delivered. Strong Geospatial Intuition: You understand projections, polygons, spatial joins, and how to visualize density and patterns effectively. Engineering Mindset: You write clean, modular, maintainable Python code designed for production — not one-off scripts. Experience: 3+ years in a Data Science or Data Engineering role, with hands-on ownership of production systems. Infrastructure Proficiency: Solid experience with Docker and Kubernetes, including debugging and troubleshooting live environments. Database Expertise: Advanced SQL, especially with PostGIS or similar spatial extensions. Experience with ClickHouse is a strong plus. Orchestration Mastery: Deep hands-on experience with Airflow for building and maintaining reliable data pipelines. Fully remote role with flexible working hours. Close collaboration with leadership and go-to-market teams. International, ambitious, and collaborative team culture. Benefits Fully remote role with flexible working hours. Close collaboration with leadership and go-to-market teams. International, ambitious, and collaborative team culture.

Location: United States; Canada; Australia; New Zealand; United Kingdom About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Job We are looking for accomplished Competitive Data Scientists, Machine Learning Engineers, and Applied Mathematics Specialists with a proven track record in data science competitions, particularly on Kaggle and similar platforms. This opportunity is designed for candidates who have achieved at least Kaggle Expert ranking, including Masters and Grandmasters, and can demonstrate strong practical skills in machine learning, statistical modeling, quantitative reasoning, and programming. You will use your competition experience to design challenging, reproducible technical tasks that require genuine problem-solving rather than plausible-looking answers. Your work will involve building execution environments, implementing verified reference solutions, developing reliable evaluation criteria, and testing how AI Coding Agents perform against complex data science challenges. A verifiable Kaggle profile with Expert, Master, or Grandmaster ranking is a key qualification. Strong practical coding and debugging abilities are essential. Previous AI training experience is not required. LOCATION: Remote – US, Canada, Australia, New Zealand, UK WORKLOAD: Approximately 15 hours per week. SCHEDULE: Flexible; choose your working days and hours, including weekends. PROJECT: AI training focused on competitive data science, machine learning, and reproducible technical problem-solving. AI EXPERIENCE: Not required. Key Responsibilities Design challenging data science and machine learning tasks inspired by real-world problems and competitive data science scenarios. Build reproducible execution environments, prepare datasets, configure dependencies, and implement verified reference solutions. Develop automated tests and evaluation criteria that distinguish correct solutions from incomplete, incorrect, or unreliable approaches. Use coding agents to execute assigned tasks, investigate their failures, and evaluate their problem-solving capabilities. Identify implementation errors, flawed assumptions, data leakage, statistical inconsistencies, and other issues affecting solution correctness. Apply competitive data science techniques and mathematical reasoning to validate solutions. Ensure tasks have clear requirements, reliable expected outputs, and reproducible verification procedures. Refine task difficulty and evaluation methods based on observed coding-agent performance. Document task specifications, solution methodologies, technical reasoning, and verification steps. Review and improve task quality to ensure scientific and computational accuracy. Requirements Demonstrated experience in competitive data science, machine learning, quantitative modeling, or applied mathematics. Kaggle profile with at least Expert ranking (Master or Grandmaster ranking strongly valued). Ability to provide a verifiable Kaggle profile demonstrating competition achievements and ranking. Strong understanding of machine learning, statistical modeling, data analysis, and quantitative problem-solving. Practical programming ability, particularly in Python. Ability to independently implement, test, debug, and verify data science solutions. Experience working with datasets, computational environments, and relevant machine learning libraries. Strong analytical reasoning and the ability to explain technical decisions. Ability to identify data leakage, implementation flaws, and methodological errors. Excellent written communication and technical documentation skills. Professional English communication skills.

Location: Europe Workplace: Remote OUR VISION & PRODUCTS 🚀 EverAI — Building the Future of AI Companionship One of the Top 15 Largest & Fastest-Growing AI Companies in the World 50 Million Users in 2 years — Help Us Reach 100M first, 500M next At EverAI, we’re shaping what it means to connect with AI. With 50 million users and counting, we're not just building products — we're creating entirely new categories. Our flagship product is the world’s largest AI companionship platform, redefining relationships for millions. It is governed by our proprietary moderation system, EverGuard — an internal AI designed to ensure everything we build is safe, ethical, and human-first. And we’re only just getting started! OUR TEAM We are an enthusiastic, passionate and hardworking team of ≈ 100 people. Our founding team has strong entrepreneurial experience building and scaling web products from 0 to IPO. Alexis Soulopoulos [CEO] • 10+ years in Tech Executive Leadership • Co-Founder Mad Paws Holdings (from 0 to IPO) • Forbes 30 under 30 + Deloitte TechFast50 ’22 & ‘23 Michael Monin [Co-founder & CTO] • 10+ years as CTO / COO (web2/web3), 3 years in AI/LLM • Serial-entrepreneur: MTK Digital (exited / 0->$20m revenue) and Zipchat (AI Chatbot for E-commerce brands) Thomas Lacroix [Co-founder & CMO] • 8+ years in Customer Acquisition & E-commerce Growth • Serial-entrepreneur: Curatible (sold to Blackstone) and MTK Digital (exited / 0->$20m revenue) Maruša Fasano [CFO/Legal] • 25+ years in Finance, Strategy, M&A • Ex-CFO/M&A @Curatible (exited to Blackstone) • Ex-President of the Board @SotremoSA (exited) • Co-founder/CFO @SoftOne (exited) YOUR ROLE Most LLM roles stop at experimentation. Here, the models you improve power experiences for 50M+ users. Most of our traffic runs on self-hosted, fine-tuned open-weight models. On our internal roleplay evals, our smaller fine-tuned models can outperform much larger off-the-shelf models. Your job is to make them better. You’ll join a small AI team led by Marco, our LLM Tech Lead, working closely with our CTO, Product, Web and DevOps teams. You’ll work across data, post-training, evals, and model behavior, taking ideas from failure analysis to production. The problems are real: personalization, character consistency, long conversations, better evals, multilingual behavior, and faster training cycles. If you want to shape model behavior at consumer scale, this is the role. KEY RESPONSIBILITIES - Fine-tune open-weight LLMs using SFT, LoRA/QLoRA and post-training methods such as DPO and GRPO - Diagnose model failures and turn them into experiments. Decide whether the answer is better data, post-training, prompting or classifiers - Build training data and evals that directly shape what gets trained next - Improve model behavior across personalization, consistency, tone, multilingual and group interactions - Ship to production and use real user behavior to guide the next iteration - Speed up the training loop so experiments are faster, easier to compare, and more reproducible YOUR QUALIFICATIONS - You’ve personally post-trained open-weight LLMs for a user-facing product and shipped them to production - You’ve worked where AI was core to the product or business, not an experimental side feature - Fine-tuning is core to your experience - Strong Python, PyTorch and Hugging Face, with hands-on GPU training and debugging - You build data and evals yourself and make decisions from evidence - You can take work beyond notebooks into production - You use AI tools daily in your own engineering work - You are fully comfortable working with uncensored models and explicit NSFW content, including harmful material when developing and evaluating moderation system BONUS POINTS - Experience with conversational AI, roleplay products, or highly personalized consumer AI - Multilingual LLMs - Moderation, safety, or adversarial testing - Multimodal models - Open-source LLM / post-training contributions WHY EVERAI? 📈 Exponential Growth: From 50M users in 2 years, to 100M next — and 500M beyond 🚀 Track Record of Category-Creating Innovation: We consistently launch world-first AI applications — setting the pace, not following it 🌍 Global Impact: Top-tier user growth, real-world adoption, and cultural relevance 🧠 Proven Leadership: A senior team that’s launched, scaled, and exited & IPO’d multiple scale ups — now fully focused on reshaping AI companionship 👥 Elite Remote Team: 100% remote and built to win — world-class talent from Tier 1 tech companies, with a culture of ownership, velocity, and radical creativity 🛡️ Ethical Core: Our AI ecosystem is governed by EverGuard, our proprietary AI moderation technology, ensuring responsible development at scale WHAT WE OFFER ✍️ Contract Type: We prefer B2B, but we’re flexible, what matters is long-term commitment and impact 📍 Work From Anywhere: Fully remote. Choose the environment where you do your best work 🏝️ Paid Time Off: 4 weeks (20 working days) of PTO per year to recharge and reset 👨‍👩‍👧‍👦 Annual Gathering: A yearly in-person meetup to connect, brainstorm, and celebrate wins together ❤️‍🩹 Health & Wellness Support: Monthly allowance of 100 USD for health insurance expenses & access to unlimited 1:1 sessions with psychologists and lifestyle experts through OpenUp (also available to up to three of your family members) 🏢 Co-Working Space Budget: Work from a co-working space up to twice per month (35 EUR / 40 USD per visit) to stay inspired and connected 📚 Learning Budget: Dedicated funds to support your professional growth: courses, books, conferences, events, or certifications 💻 Company Laptop & Equipment: Laptop provided + monitor budget up to 250 USD + keyboard/mouse/mic/headphones budget up to 150 USD ⚡ AI Tools Access: Premium access to ChatGPT, Hugging Face, Claude Code, and any other tool needed to excel at your job, power your ideas and workflows 🎯 TOP TIER TALENT IS OUR MULTIPLIER We’re a fully remote group of A-players from Tier 1 tech, led by an exec team who’ve launched, scaled, and exited multiple companies. We move fast, and care deeply about what we build — and who we build it with. We’re looking for exceptional talent ready to ship & distribute world-first AI products at scale, fast, and co-create with us this category-defining business. If that’s you — reach out and apply!

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Research Scientist, Discovery (Engineering & Applied Sciences) About the Team Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific and technological frontier. About the role On the Discovery team at Anthropic, we're expanding our work on engineering and the applied sciences. Our goal is to contribute to training Claude to invent, design, and engineer across every discipline and scale, reliably and safely: new manufacturing and experimental techniques, processes, materials, devices, instruments, machines, energy systems, and factories. We want this work to accelerate science and technology and benefit people widely. To achieve these goals, we're seeking exceptional machine learning researchers with deep expertise in training and evaluating frontier models. In this role, you'll work end to end across the model stack, developing training environments, rewards, data, and evaluations that measure and improve Claude's performance on complex engineering and scientific tasks, and carrying what works into production training runs. You'll collaborate closely with researchers, engineers, and partners across Anthropic and industry to build AI systems that can engage in all phases of research and development, and that learn from real-world feedback. A background in engineering or the sciences is a plus but not required. Key responsibilities Design, build, and study reinforcement learning environments, rewards, and training data that improve Claude's capabilities in engineering and the applied sciences Create benchmarks and evaluation frameworks that measure model performance on realistic engineering and scientific workflows Develop simulation-based training environments and verifiers, working with domain experts to validate them against real-world data Develop approaches to long-horizon task completion, multi-agent collaboration, and complex reasoning essential for real engineering and research work Research how models can learn efficiently from experiments and real-world feedback. Study how environments and rewards behave in large-scale training, including robustness, calibration, and generalization, so that training produces models that are both capable and well aligned Scale research ideas from prototype to production training runs Collaborate with domain experts and industry partners to build and test what Claude invents, designs, and engineers, and turn that feedback into training and evaluation signal Help chart how Claude’s engineering capabilities reach real-world use: where they matter most, what stands in the way of adoption, and how that should shape research priorities You may be a good fit if you have: Proficiency in Python and hands-on experience with a modern machine learning framework such as PyTorch or JAX Hands-on experience training or evaluating machine learning models Track record of machine learning research: forming hypotheses, designing and running experiments, and drawing sound conclusions from the results Ability to implement your own research ideas and diagnose problems in complex systems Ability to learn unfamiliar technical fields quickly and work productively with experts in them Clear communication and a collaborative approach to solving problems Excitement about improving Claude’s scientific capabilities and the downstream impact on science and technology Commitment to developing AI that is safe and beneficial Strong candidates may also have: Experience with reinforcement learning and pre-training on large language models, including environment and reward design Familiarity with large-scale language model training, evaluation, and inference pipelines Published research or practical experience in AI for science or engineering, or in long-horizon reasoning A degree or work experience in an engineering or applied science discipline, such as mechanical, electrical, materials, chemical, aerospace, systems, or manufacturing engineering, applied physics, or computational simulation Experience building simulators, surrogate models, or simulation-based environments and verifiers, and calibrating and validating them against real-world data Experience with large-scale data pipelines and technical or multimodal datasets Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 — $500,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Москва Мы исследовательское подразделение, которое отвечает в Сбере за «человека»: поведение, психология, тренды, изменения, особенности восприятия и мышления. Наша ключевая задача: исследовать и рассказывать, что происходит и будет происходить с человеком, как эти изменения переложить в продукт, коммуникации, HR-процессы, чтобы они стали лучше.  Работа в Лаборатории – это возможность прикоснуться практически к любому бизнес-подразделению экосистемы Сбера, к большим данным Сбера, к клиентской базе более 100 млн человек. Мы ищем ML Developer'а — человека, который будет реализовывать ML-проекты Лаборатории: обучение и дообучение моделей, построение data-пайплайнов, вывод решений в production. В отличие от LLM-генералистов команды, которые быстро вайбкодят прототипы, вы будете отвечать за то, чтобы модели работали надёжно, воспроизводимо и масштабируемо, а также дорабатывать прототипы до продакшен решений. Вы рамках Лаборатории вы будете в команде “больших данных и гиперперсонализации”, всего около 25 человек, из которых DS-команда – порядка 10 человек, остальные – исследователи, психологи и социологи. Лаборатория работает на стыке поведенческой науки, данных и AI. Мы строим модели, которые описывают и предсказывают поведение человека — от сегментации клиентов до цифровых двойников. ML Developer — это тот, кто превращает исследовательские гипотезы в работающие модели и надёжную инфраструктуру вокруг них. Наш технологический стек: * ML/DL: HuggingFace Transformers, SentenceTransformers, CatBoost, XGBoost * LLM & Vector Search: локальные модели (Llama 3, Qwen), API (OpenAI), FAISS * Data & Infra: ClickHouse, Docker, FastAPI, Postgres, RabbitMQ, MongoDB * MLOps & Orchestration: MLflow, W&B, Prefect, Dagster, Ray, Prometheus, OpenTelemetry * Инструменты: Hydra, Poetry. Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика ждите сообщение от него в мессенджере, диалог займёт примерно 10 минут. Задача AI-рекрутера — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. AI-рекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным для всех! Обязанности ### Чем предстоит заниматься: * классический ML/NLP: разработка и обучение моделей для анализа текстов, поведенческих данных, психометрических профилей. * кластеризация, классификация, NER, sentiment — всё, что требуется для исследований на данных. * LLM-интеграция: fine-tuning и дообучение языковых моделей под задачи Лаборатории, построение embedding-пайплайнов, проектирование RAG-архитектур с графовыми и векторными БД. * работа с промптами на уровне системной архитектуры, а не ad hoc экспериментов * Production & MLOps: вывод моделей из ноутбуков в production — контейнеризация, CI/CD, мониторинг, версионирование моделей и данных * построение воспроизводимых пайплайнов обработки данных. Требования ### Наши ожидания от кандидата: ML & NLP: * практический опыт обучения и дообучения моделей (классический ML + deep learning) * уверенная работа с NLP: text classification, NER, embeddings, topic modeling, умение работать с ASR, Text-to-Speech задачами * опыт fine-tuning трансформерных моделей (HuggingFace Transformers, PEFT, LoRA, QLoRA) * понимание метрик качества моделей и умение проектировать эксперименты. LLM & Retrieval-архитектуры: * опыт работы с LLM API (OpenAI, Anthropic и др.) на уровне production-решений * построение RAG-пайплайнов: chunking-стратегии, embedding-модели, векторные и графовые БД * понимание различий между векторным поиском и BM25 * работа с LangChain / LangGraph или аналогами. Engineering & MLOps: * Python как основной язык, уверенное владение * Docker, CI/CD, базовый DevOps * опыт работы с ML-оркестраторами (MLflow, W&B, Prefect, Dagster — хотя бы один) * навыки разработки API (FastAPI / аналоги) * работа с SQL-базами (ClickHouse, Postgres) и очередями (RabbitMQ и т.п.). Софты и майндсет: * техническое образование (ML, CS, математика, инженерия) * умение переводить абстрактные исследовательские гипотезы в формализованные ML-задачи * способность объяснять технические решения нетехнической аудитории (исследователям, бизнесу) * интерес к поведенческой науке, психологии — или хотя бы готовность погружаться. Будет плюсом: * опыт работы с распределёнными вычислениями (Ray, DeepSpeed) * знакомство с Prometheus, OpenTelemetry, observability-стеком * опыт работы с графовыми базами данных (Neo4j и т.п.) * контроль стоимости и latency AI-вызовов * публикации, pet-проекты, вклад в open-source * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия ### Мы предлагаем: * комфортный современный офис г. Москва, рядом с метро Кутузовская * офисный формат работы: 5/2 с 10 до 19, возможен гибрид (1-2 дня в неделю вне офиса) * 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.

Location: Москва **GigaChat увидит мир через данные, которые соберёте вы.** За GigaChat стоит одна из крупнейших ML-команд в России, которая обучает LLM и VLM полным циклом — от претрейна до релиза. Наша команда отвечает за то, с чего начинается любая сильная модель, — за данные: мы собираем визуальный корпус претрейна — ищем источники, фильтруем и дедуплицируем сырьё, ставим эксперименты и меряем влияние на бенчмарки. Именно состав и качество претрейн-данных определяют, каким получится финальный результат, и у вас будет возможность влиять на него напрямую. Обязанности * ставить эксперименты: формулировать гипотезы, какие данные и в каких пропорциях улучшат модель, и проверять их: продумывать сетап и бейзлайны, запускать обучающие прогоны, по замерам решать, что попадёт в финальную смесь * автоматизировать подготовку данных: выстраивать очистку, дедупликацию и валидацию так, чтобы любой новый датасет без ручной доводки приходил в готовый к обучению вид * добывать редкие и дефицитные домены: обучать классификаторы и кластеризаторы, которые вылавливают ценные сэмплы из сырых данных, и нарезать корпус на чистые датасеты по таксономиям * собирать данные разных форматов: генерация VQA, синтетические задачи, interleaved-данные — каждый формат устроен по-своему и требует своего подхода * обрабатывать миллиарды изображений: писать распределённый код, который прогоняет пайплайны на всём корпусе быстро и стабильно * находить слабые места в данных: разбираться, где модель проседает из-за корпуса — чего не хватает, что зашумлено или разбалансировано, — и исправлять это * развивать контур оценки: подключать свежие бенчмарки, следить за чистотой методики и не пускать тестовые данные в обучающий корпус Требования **Кто нам подходит:** Человек, который сможет провести данные от поиска источников до состава финальной смеси. Главное — умение ставить эксперименты и делать из них честные выводы. Опыт именно с VLM или LLM не обязателен: специфика осваивается быстрее, чем культура эксперимента **Что мы хотели бы видеть:** * хорошая база ML, твёрдое знание CV и NLP * понимание, как данные влияют на качество модели, и привычка при странностях в метриках первым делом смотреть в данные * уверенный Python и опыт обучения моделей на PyTorch * умение спланировать эксперимент и довести его до вывода, которому можно верить * самостоятельность и проактивность: берёте тему, разбираетесь, доводите до результата **Плюсом будут:** * опыт построения дата-пайплайнов и процессов контроля качества данных * опыт с распределённой обработкой (Spark, Dask, Airflow, YTsaurus) * опыт проведения ML-экспериментов на больших объёмах * публикации на профильных конференциях. Мы открыты к кандидатам разного уровня, и попадать во все пункты не обязательно. Если задачи откликаются — напишите нам, остальное обсудим. Условия **Что мы предлагаем:** * опыт претрейна из первых рук. Команд, обучающих VLM полным циклом, в стране единицы — вы пройдёте весь путь от сырых данных до готовой модели * вычислительные ресурсы под ваши эксперименты. Один из самых больших GPU-кластеров в России: проверка гипотезы о данных стоит обучающего прогона, и на такие прогоны вычислений у нас хватает * инфраструктура, которая уже работает. S3, YTsaurus, Airflow, DataLens, GitLab CI, wandb — всё для данных и экспериментов на месте, время уходит на исследования, а не на обвязку * задачи, на которые ещё нет ответа. Как собрать лучшие визуальные данные для претрейна, пока не знает почти никто в мире — разбираться предстоит в том числе вам * короткий путь от гипотезы до модели. Данные, которые вы отберёте, и гипотезы, которые подтвердятся, уходят в обучение следующей модели — а ей пользуются миллионы людей * комфортный современный офис рядом с м. Кутузовская * возможность выбрать удобный график – офис/гибрид/удаленка (в РФ) * 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 * ипотека для сотрудников по дисконтной прогамме * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Ищем DevOps/MLOps инженера в проектную команду для сопровождения и развития платформы машинного обучения и AI-сервисов (LLM, агенты, чат-боты). развёртыванием ML-моделей и AI-сервисов, локальных моделей (LLM, ML-модели) в различных средах;

— Разработка и обучение моделей для оптимизации генерации ZK-доказательств и работы ZK-сопроцессоров.

Plaid is looking for a Machine Learning Engineer to build, deploy, and monitor credit machine learning systems.

- Build evaluation frameworks for AI agents, including datasets, sandbox environments and metrics for real-world tasks. - Hands-on experience building, evaluating or improving LLM models or agents (professional, research, open-source or personal projects).

Задачи: разработка ML-моделей и алгоритмов оптимизации, включая алгоритм приоритизации и ранжирования вариантов замен с формированием трёх наиболее подходящих опций и объяснением критериев выбора; сбор, обработка, нормализация и анализ данных из корпоративных источников, подготовка датасетов и витрин для ML-моделей;