Вакансии

Location: Charlotte, United States TravelConfirm, Inc. dba Dev-Pro.net seeks Head of Artificial Intelligence (AI) Practice for Charlotte, North Carolina, telecommuting from anywhere in the U.S. is permissible 100% of the time. Send resume to Anastasiia Shulzhenko at anastasiia.shulzhenko@dev.pro or apply online at www.career.dev.pro/vacancies. EOE. ✅ Responsibilities: Lead AI practices and AI Community initiatives; Conduct and manage R&D projects; Define and maintain organizational AI tooling standards; Communicate AI security concerns; Track AI progress at company level; Establish and maintain AI knowledge base; Manage delivery of AI projects for clients; Oversee AI projects delivery; Conduct project audits; Resolve project issues; Mentor teams; Lead development of internal AI solutions; Define and communicate architecture vision; Provide executive oversight for proposals and technical solutioning; Lead client discovery and solution scoping; Support business development through practice leadership; Lead technical stakeholder interviews; Oversee solution design and present solution strategy to clients; Develop AI grades and assessment criteria; Participate in staffing; and conduct AI candidate interviews. ✅ Education Requirements : Bachelor’s degree in Computer Engineering, Computer Science, Software Engineering, or a closely related field. ✅ Work Experience Requirements : 8 years of progressive professional work experience in software engineering including five (5) years of experience with system architecture and software design tools and platforms including C4 Model, UML, PlantUML, and Draw.io; 5 years of experience with major cloud platforms including AWS and Azure for AI solution architecture and deployment; 5 years of experience with technical team leadership and project delivery oversight using CI/CD and containerization tools such as GitHub Actions, Docker, Jenkins, and TeamCity; 3 years of experience with technical consulting and pre-sales support with tools including Jira and Confluence; and experience with industry-standard artificial intelligence and machine learning technologies including generative AI methodologies and AI-assisted software development tools such as Whisper and Python programming for artificial intelligence/machine learning (AI/ML) development. ✅ Alternative Requirements : Master’s degree in Computer Engineering, Computer Science, Software Engineering or a closely related field and three (3) years of professional work experience in software engineering including two (2) years of experience with system architecture and software design tools and platforms including C4 Model, UML, PlantUML, and Draw.io; 2 years of experience with major cloud platforms including AWS and Azure for AI solution architecture and deployment; 2 years of experience with technical team leadership and project delivery oversight using CI/CD and containerization tools such as GitHub Actions, Docker, Jenkins, and TeamCity; 1 year of experience with technical consulting and pre-sales support with tools including Jira and Confluence; and experience with industry-standard artificial intelligence and machine learning technologies including generative AI methodologies and AI-assisted software development tools such as Whisper and Python programming for artificial intelligence/machine learning (AI/ML) development. Will accept any suitable combination of education, training, or experience.

Location: Poland; Romania; Slovenia; Slovakia; Greece; Portugal Intetics Inc. , a leading American technology company specializing in custom software application development, distributed professional teams creation, software product quality assessment, and "all-things-digital" solutions, is looking for an AI Developer (LLM / RAG / Agentic AI) to join its innovative team. Project Responsibilities Design, build, and maintain LLM-based agents in production environments, both customer-facing and internal. Build MCP servers on top of existing databases and production systems, and orchestrate multiple internal and external MCP servers under a unified agent framework. Design and implement memory, context management, and RAG architectures for production use. Establish evaluation frameworks, handle edge cases, and continuously improve agent performance after release. Work within architectures that combine databases, APIs, interfaces, agents, and orchestration applications. Develop AI solutions using Amazon Bedrock and/or Microsoft Foundry. Collaborate with cross-functional teams to deliver scalable and reliable AI-powered products. Candidate's Portrait We are looking for a hands-on AI Engineer (Middle+ to Senior level) who has successfully delivered AI agents into production and can clearly explain implementation challenges, failure cases, and optimization approaches. The ideal candidate is passionate about AI, actively explores new tools and models, and has a proven track record building intelligent systems beyond experimentation. 1.5-2+ years of hands-on experience with LLMs and AI agents in production environments. Strong background in Machine Learning, Python development, or Data Science. Self-driven, innovative, and continuously learning emerging AI technologies. Must-Haves 6+ years of overall experience in IT as a Software Engineer, ML Engineer, or AI Engineer. Hands-on production experience with Amazon Bedrock and/or Microsoft Foundry (Azure AI Foundry, Azure OpenAI). Proven experience building and deploying LLM-based agents to production, including customer-facing conversational AI and internal workflow automation agents. Strong experience with at least one agent framework: LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, Strands Agents, or OpenAI Agents SDK . Deep MCP expertise, including building MCP servers on top of existing databases and systems, integrating external MCP servers, and orchestrating multiple MCP services under a single agent. Strong experience with production-grade RAG, memory, and context engineering, including: Embeddings Chunking strategies Hybrid search Reranking Vector databases Short-term and long-term agent memory Practical experience implementing agentic patterns such as: ReAct Planner-Executor Reflection Router Supervisor Multi-Agent Systems Human-in-the-Loop Experience with LLM evaluation, guardrails, monitoring, and iterative optimization based on production usage and failure analysis. Strong understanding of system architecture involving databases, APIs, agents, and orchestration applications. Hands-on experience with Docker and CI/CD pipelines. Nice-to-Have Experience in fintech, trading, investing, brokerage, or cryptocurrency-related products. Background in regulated industries with strong requirements around compliance, data protection, and AI output auditability. Broader AWS or Azure cloud expertise beyond AI services. Experience with LLM evaluation and observability platforms such as LangSmith, Langfuse, Ragas , or custom evaluation pipelines. Advanced practical expertise with modern agentic AI development tools and rapid prototyping approaches.

Задачи: — Анализ трафика всех источников — Анализ и оптимизация воронок — Интерпретация данных о поведении пользователей, формирование и донесение выводов и дальнейших действий для оптимизации сайтов и рекламных кампаний — Формирование рекомендаций и тестирование предложенных гипотез, постоянный поиск точек роста качества взаимодействия — Формирование отчетности Требования: — Опыт работы web аналитиком — Опыт анализа данных — Уверенный SQL, Excel — Опыт работы с инструментами веб аналитики (Яндекс Метрика / GA4) — Знание python на уровне medium — Опыт разработки сложных отчетов и моделей из различных источников данных — Опыт работы с BI — Понимание маркетинговых и продуктовых метрик Условия: — Работа в стабильной компании с возможностью профессионального и карьерного роста — Расширенную программу ДМС со стоматологией — Официальное трудоустройство, социальные гарантии: оплачиваемый отпуск, больничный — Полностью официальная заработная плата, выплаты 2 раза в месяц — График работы: 5\2 c 9:00- до 18:00 — Офис в 10 минутах от метро — Скидки на медицинское обслуживание в сети клиник для сотрудников и членов семьи — Насыщенная социальная жизнь (корпоративные мероприятия, праздники)

Задачи: — Поддерживать и обновлять существующие дашборды для маркетинговых команд; — Выполнять ad-hoc выгрузки и запросы от маркетологов (пуши, баннеры, рекламные кампании); — Ежедневно мониторить алерты по рекламным сущностям, первичная диагностика проблем; — Поддерживать витрины данных в Airflow: обновление, доработка по готовым ТЗ; — Документировать витрины, дашборды и аналитические процессы; — Первично коммуницировать с заказчиками по типовым вопросам; — Автоматизировать мелкие рутинные отчёты; — Участвовать в передаче офлайн-конверсий через S2S (server-to-server) и measurement protocol. Требования: — Опыт работы в маркетинговой аналитике от 2 лет; — Уверенное владение SQL (джойны, подзапросы, массивы, оконные функции); — Опыт работы с СУБД Clickhouse будет плюсом; — Понимание маркетинговых метрик: ДРР, ROMI, CPA, LTV, конверсии, воронки; — Понимание принципов атрибуции; — Знание рекламных систем: Яндекс.Директ, Google Ads, VK Реклама и т.п.; — Опыт работы с системами: Яндекс.Метрика, Google Analytics; — Опыт работы с BI-инструментами (Redash, Tableau, PowerBI или аналоги); — Понимание принципов работы с AI-инструментами (ChatGPT, Claude, Copilot и др.) и умение применять их для ускорения рутинных задач: генерация SQL-запросов, написание регулярных выражений, помощь в анализе данных, подготовка документации; — Понимание или опыт передачи офлайн-конверсий через S2S (server-to-server) - осознание, зачем это нужно и как влияет на оценку эффективности рекламы; — Аналитическое мышление, умение задавать вопросы и самостоятельно разбираться в задаче. Будет плюсом: — Опыт работы с Airflow (или готовность быстро освоить); — Python на уровне работы с данными (pandas); — Практический опыт использования AI-инструментов в ежедневной работе: автоматизация отчетности, генерация кода, анализ больших текстов, работа с LLM для построения гипотез; — Практический опыт настройки или участия в передаче офлайн-конверсий через S2S (measurement protocol), работа с API рекламных систем для передачи конверсий.

Задачи: — Создавать и внедрять CI/CD-проекты для систем в Банке — Создавать скрипты для автоматизации процессов разработки и сопровождения Требования: — Опыт построения CI/CD для систем — Знание Gitlab-CI или другой CI-системы — Хорошее знание Linux, bash, GIT — Знание технологии Docker/Openshift — Желательно иметь навык работы с Ansible/AWX или другой системы управления конфигурациями — Желательно иметь опыт написания Python (или Groovy)-скриптов — Желательно иметь опыт работы с базами данных, начальное знание SQL — Умение общаться с другими ИТ-командами по техническим вопросам Условия: — ДМС после испытательного срока и льготное страхование членов семьи — Гибридный формат и гибкое начало рабочего дня — Оплачиваемый больничный и отпуск на важные даты — Улучшенные условия на кредиты, кредитные карты, вклады и другие продукты банка — Скидки на обучение английскому языку от Skyeng, фитнес, услуги здоровья и красоты — Корпоративное обучение: курсы, тренинги, вебинары и базы знаний — Бонус за каждого, кто присоединится к команде благодаря сотруднику — Развитая программа заботы о благополучии и ментальном здоровье — Насыщенная корпоративная жизнь, награждение лучших сотрудников и новогодние подарки детям

Задачи: ∙ Обеспечение бесперебойной работы федеральных информационных систем; ∙ Оперативное решение возникающих проблем; ∙ Работа с системами виртуализации Hyper-V, VMware и OpenVZ; ∙ Работа с базами данных MSSQL, PostgreSQL, MySQL; ∙ Настройка, адаптация и модернизация процесса развертывания CI-CD; ∙ Сборка с GitLab; ∙ Документирование рабочего процесса (фиксация и описание всех выполняемых задач); ∙ Полный задач гибкий и может расширяться в зависимости от вашей экспертизы и желания влиять на процессы. Требования: ∙ Высшее техническое образование; ∙ Опыт работы LINUX-системным администратором не менее пяти лет; ∙ Опыт написания скриптов bash, powershell, python; ∙ Опыт использования Ansible; ∙ Знания по администрированию серверных операционных систем семейства Linux; ∙ Понимание принципов работы k8s, Docker, CRI-O; ∙ Опыт администрирования PostgreSQL; ∙ Опыт работы с Kafka, Redis, Clickhouse; ∙ Опыт работы с GitLab; ∙ Настройка и интеграция с другими элементами, необходимыми для реализации CI/CD; ∙ Знание основных протоколов передачи данных; ∙ Знание принципов построения TCP/IP сети; ∙ Знание английского языка на уровне, достаточном для чтения профессиональной литературы; ∙ Умение самостоятельно осваивать различные технические концепции; ∙ Умение самостоятельно решать возникающие вопросы и задачи; ∙ Создание собственных скриптов для мониторинга компонентов системы. Будет преимуществом: ∙ Опыт развертывания и глубокого обслуживания СУБД (Microsoft SQL Server, Postgres); ∙ Опыт работы с технологиями виртуализации; ∙ Опыт работы с системами мониторинга (SCOM, Zabbix, Grafana); ∙ Навыки автоматизации рутинных операций; ∙ Желание учиться и развиваться вместе с компанией. Условия: ∙ Все льготы IT-отрасли: компания аккредитована Минцифры РФ (отсрочка от призыва, льготная IT-ипотека, налоговые преимущества); ∙ Стабильность: официальная «белая» заработная плата, оформление по ТК РФ с первого рабочего дня; ∙ Комфорт: современный офис кабинетного типа у м. Нагатинская, оборудованный кофе-зонами и всем необходимым для продуктивной работы; ∙ Атмосфера: лояльное руководство без микроменеджмента, регулярные корпоративные мероприятия и неформальное общение; ∙ Развитие: возможность обучения и получения сертификатов за счет компании (при производственной необходимости); ∙ График работы: 5/2 с 9:30 до 18:15; ∙ Работа строго в офисе, удаленный формат не рассматривается.

Задачи: — Разрабатывать и поддерживать автотесты для бэкенд-сервисов на Python/Go. — Тестировать синхронные и асинхронные взаимодействия: REST API, gRPC, очереди сообщений (Kafka). — Выстраивать и поддерживать процесс предрелизного тестирования. — Проверять целостность, согласованность и миграции данных в СУБД (PostgreSQL, Redis). — Интегрировать автотесты в пайплайны CI/CD (GitLab CI), разбирать падения, бороться с flaky-тестами. — Локализовывать проблемы на стыках микросервисов с помощью распределенной трассировки и логов. — Участвовать в проектировании архитектуры тестирования новых фичей вместе с командой бэкенд-разработки. Требования: — Опыт разработки автотестов для бэкенда на Python или Go. — Понимание REST API, gRPC и асинхронных очередей сообщений (Kafka). — Базовое знание PostgreSQL или Redis из автотестов. — Опыт работы с CI/CD (GitLab CI) и умение разбирать падения тестов.

Задачи: — Участие в операционной деятельности команды: дейли-митинги, спринт-планирования, ретроспективы, проведение демо для заказчика; — Контроль соблюдения сроков и качества результатов по всем активным проектам; — Проектирование и внедрение LLM решений: NER/классификация/обогащение, semantic search и RAG пайплайны, подготовка датасетов, fine tuning, улучшение метрик; — Ведение проектной документации в соответствии с принятыми стандартами и регламентами компании. Требования: — Опыт DS/ML (3+ лет) и общий опыт в IT около 6 лет, с фокусом на end-to-end внедрения AI/ML решений; — Опыт оценки трудозатрат для комплексных AI-проектов; — Инструменты управления: Jira, Confluence или аналоги для трекинга задач, ведения документации и управления бэклогом; — Понимание полного жизненного цикла ML-проекта: сбор данных, разметка, обучение, валидация, деплой, мониторинг; — Понимание принципов работы LLM, GenAI, RAG и агентных систем — на уровне, достаточном для оценки рисков; — Понимание принципов MLOps: CI/CD для моделей, версионирование данных, автоматизация пайплайнов; — Высокий уровень владения языком Python и опыт разработки production решений и интеграций с корпоративными системами; — Практика работы с корпоративными базами данных (PostgreSQL, ClickHouse, Vertica или аналогичными) и построением витрин/ETL (Airflow, Kafka и т.п.). Условия: — работа в аккредитованной IT-компании (льготная ипотека и проч.); — оформление по ТК РФ и его полное соблюдение (оплачиваемые отпуска, больничные); — ДМС со стоматологией с первых недель работы (а не после испытательного срока).

Команда SberAds разрабатывает предиктивные модели и высоконагруженные real-time сервисы для интернет-рекламы. Мы проходим полный цикл аукциона: от идентификации пользователя и оценки запроса до выбора релевантных объявлений, ставок и показа. Наша работа напрямую повышает качество рекламы, удовлетворенность рекламодателей и эффективность SberAds. Задачи: — строить/улучшать модели для разных частей всего пайплайна — заниматься полным циклом DS/ML задач — участвовать в создании и развитии платформы рекламы — улучшать процесс построения моделей (от выдвижения гипотез до мониторинга работы модели). Требования: — знание классического ML и Deep Learning подходов — опыт промышленного использования методов RecSys — опыт разработки моделей от проблемы до сопровождения в проде — уверенный опыт разработки на Python и Pyspark-е, опыт написания продакшн-кода — знание принципов распределенной обработки данных — умение принимать решения на основании данных и аргументировать свою позицию — умение находить решения для проблем, декомпозировать, объяснять и контролировать выполнение. Условия: — офис метро Маяковская (Оружейный переулок) — гибридный формат работы — конкурентная компенсация (оклад и премии по результатам деятельности) — ДМС c первого рабочего дня, страхование от несчастных случаев и тяжелых заболеваний — материальная помощь и социальная поддержка, корпоративная пенсионная программа — дисконт-программы от компаний партнеров — бесплатный фитнес на территории работодателя — подписка Прайм с возможностью совместного использования на трёх близких — внешнее и внутреннее профессиональное обучение: семинары, тренинги, конференции, корпоративная библиотека.

Задачи: — Сбор, анализ, формализация и согласование требований для построения хранилищ данных, витрин и ETL-процессов; — Исследование источников данных, формирование требований к выгрузкам и подготовка технических спецификаций; — Анализ и корректировка кода пакетов выгрузки данных; — Подготовка и согласование документации; — Разработка и доработка логической и физической моделей данных; — Проектирование интеграций между аналитическими системами; — Постановка функциональных (ФТ) и нефункциональных (НФТ) требований к интеграциям; — Проектирование ETL-процессов внутри хранилища, включая детальные слои по методологии Data Vault 2.0 и Business Data Vault. Требования: — SQL (Greenplum/PostgreSQL) на уровне реализации витрин данных: оконные функции, агрегации, аналитические функции, администрирование; — Опыт работы аналитиком данных от 3 лет; — Опыт работы с распределенными системами (MPP); — Опыт работы с Python; — Опыт работы с GIT; — Опыт работы с Apache Airflow; — Опыт работы с Data Vault; — Опыт работы с Informatica. Условия: — Форма трудоустройства на выбор: договор оказания услуг с ИП или самозанятым; — Участие в масштабных проектах федерального и международного уровня; — Работа с передовыми технологиями; — Возможности профессионального и карьерного роста; — Полностью удаленная работа; — Регулярная и достойная оплата труда.

Задачи: — Отвечать за продуктовую, клиентскую и финансовую аналитику, находить точки роста и превращать их в понятные бизнес-решения — Влиять на решения команд продукта, маркетинга и финмодели через данные: от постановки вопросов до оценки эффекта на P&L — Погружаться в спортивный мир глазами беттора, анализировать поведение пользователей, метрики и сегменты на каждом этапе пути — Переводить инсайты о мотивации и поведении игроков в продуктовые и коммуникационные решения для повышения удержания и монетизации — Развивать клиентскую, продуктовую и финансовую аналитику в логике запросов бизнеса — Строить когортную аналитику и юнит-экономику продукта, искать связи игровых поведенческих факторов с ключевыми финансовыми метриками — Проектировать и проверять гипотезы, в том числе через A/B-тесты, помогать командам принимать продуктовые и финансовые решения на основе данных — Находить зоны роста бизнеса через анализ данных, инициировать и поддерживать оптимизацию процессов для ускорения принятия решений с прогнозируемым эффектом на P&L Требования: — Глубокое понимание методологий анализа данных и статистики, самостоятельное применение в продуктовых задачах от постановки гипотез до принятия решений — Будет плюсом опыт предиктивного моделирования и ML-подходов — Уверенное и самостоятельное написание сложных SQL-запросов и использование Python для аналитики, автоматизации отчетов и экспериментов — Опыт работы с большими объемами и разнородными источниками данных, умение наводить порядок в схемах, витринах и логах — Владение инструментами визуализации данных (Tableau, Power BI, DataLens или аналоги) и умение собирать дашборды для принятия решений; знание Tableau будет плюсом — Высокий уровень критического мышления и сильные коммуникативные навыки: умение проверять выводы, задавать неудобные вопросы к данным и объяснять сложные аналитические концепции простым языком — Умение работать в связке с продуктом и бизнес-командами, принимать решения командно Условия: — Процесс отбора: звонок с рекрутером, интервью с руководителем, финальная встреча в офисе, собеседование и оффер.

Задачи: — Разработка и оптимизация фронтенда для платформы финансовых инструментов; — Разработка API для взаимодействия с бэкендом; — Оптимизация производительности фронтенда системы; — Нагрузочное тестирование системы, бенчмаркинг, выявление и оптимизация узких мест совместно с командой бэкенд разработки; — Участие в проектировании архитектуры системы. Требования: — 5+ лет коммерческого опыта с React/TypeScript; — Опыт разработки фронтенд компонентов в React Storybook; — Опыт адаптации фронтенда под требования тестирования через Selenium; — Понимание принципов работы производных финансовых инструментов; — Готовность к изучению новых технологий; — Знание .NET, Python для эффективного взаимодействия с бэкендом (желательно). Условия: — Гибридный формат работы; — Конкурентная заработная плата; — Профессиональное обучение и развитие; — Добровольное медицинское страхование сотрудника и несовершеннолетних членов семьи, страхование жизни и здоровья.

Задачи: — Владеть портфелем из 10-15 стратегических аккаунтов. — Быть основным техническим контактом для API-партнеров, информировать заинтересованные стороны и доводить вопросы до решения. — Поддерживать технические оценки. — Участвовать в продажах, демонстрировать возможности API, понимать требования партнера и объяснять, как будет работать интеграция. — Руководить внедрением партнеров: превращать требования в четкий план интеграции, сопровождать разработчиков на этапе настройки и тестирования, отслеживать прогресс до запуска. — Самостоятельно устранять неполадки: исследовать запросы, ответы, логи, проблемы с аутентификацией и поведение интеграции до эскалации в инженерный отдел. — Тесно сотрудничать с инженерной командой, предоставляя четкие шаги для воспроизведения, релевантные доказательства и информацию о влиянии на клиента при необходимости глубокого расследования. — Улучшать опыт разработчиков: создавать и поддерживать руководства по API, коллекции Postman, примеры кода, FAQ и инструкции по онбордингу. — Выявлять повторяющиеся проблемы, делиться отзывами партнеров и шаблонами интеграций для улучшения продукта. — Вести усилия по расширению: кросс-продажи новых функций и продуктов как естественное продолжение отношений, а не отдельное действие. Требования: — Опыт 4+ лет в технической роли с взаимодействием с клиентами или эквивалентный опыт в компаниях с API-ориентированным или девтулз-направлением. — Практическое понимание REST API, HTTP, JSON, аутентификации, вебхуков, пагинации и ограничений по скорости. — Умение работать с Postman или curl, читать логи, писать или адаптировать небольшие примеры кода на JavaScript или Python. — Опыт продвижения внедрения внутри сложных организаций, умение работать с разными уровнями клиентов. — Сильная исполнительность и умение доводить вопросы до конца. — Опыт работы в стартапе с численностью менее 50 человек. — Свободное владение английским языком, умение объяснять технические вопросы как разработчикам, так и нетехническим заинтересованным лицам. — Ответственный подход и менталитет владельца. Условия: — Удаленная работа из любой точки мира. — Стандартные рабочие часы: понедельник–пятница, 8:00–17:00 по восточному времени США с перерывом на обед. — Возможны редкие звонки вне стандартных часов для партнеров из Азиатско-Тихоокеанского региона и ключевых этапов внедрения. — Полная занятость.

Location: Москва Мы - команда юнита "Транзакционный банкинг", и наша задача – мотивировать клиентов все больше пользоваться Сбером. Для этого мы проводим клиентские исследования, занимаемся продуктовой аналитикой, генерируем гипотезы по банковским и экосистемным продуктам, проводим А/B тесты для их подтверждения, запускаем портфельные кампании на всю аудиторию Банка, помогая привлекать и удерживать клиентов в контуре банка. Мы ищем аналитика данных под задачи поиска инсайтов в данных, а также подготовки целевой аудитории для запуска пилотных проектов по изменению транзакционного поведения клиентов. Обязанности ### Обязанности: *  сегментация клиентской базы, исследование зависимостей на большом объеме информации *  формирование целевой аудитории кампании, подготовка целевых и контрольных групп для проведения А/В теста * подведение итогов и анализ эффективности проведенных пилотов * подготовка предложений и формулирование бизнес-гипотез на основании анализа клиентской базы и проведенных компаний. Требования ### Требования: * высшее образование (физика, математика, экономика/финансы, ИТ) * опыт работы аналитиком данных / продуктовым аналитиком / лист менеджером в банковской сфере от 1-года лет * продвинутый уровень SQL, Python * уверенные знания математической статистики и принципов А/В-тестирования; * знание банковских продуктов и процессов. Условия ### Условия: * комфортный современный офис на Кутузовском, 32 * корпоративный спортзал и зоны отдыха * уникальная система обучения Сбера для профессионального и карьерного развития, семинары, тренинги, конференции * программа адаптации и помощь руководителя на старте * ДМС, страхование от несчастных случаев, социальные гарантии * 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 * корпоративная пенсионная программа * корпоративные мероприятия.

Location: New York, United States Who We Are At OKX, we believe the future will be reshaped by crypto and blockchain technology. We operate a leading global crypto platform and build products and services designed to expand access to the digital-asset ecosystem. Across our global organization, we value ownership, sound judgment, collaboration, innovation, and the ability to operate effectively in a fast-moving and continuously evolving environment. Recruiting should insert the current approved OKX corporate “Who We Are” language when the position is posted. About the Team The Fraud Investigations Team (FIT) is a First Line of Defense function within OKX’s Risk organization responsible for identifying, assessing, investigating, and helping manage significant internal and external fraud risks affecting the company, its customers, personnel, and operations. FIT conducts and supports complex investigations involving digital assets, internal and external fraud, employee misconduct, insider threats, unauthorized access or misuse of information, vendor and third-party misconduct, account compromise, social engineering, collusion, conflicts of interest, and other significant fraud-related matters. The team works closely with Legal, HR, Information Security,Engineering, Physical Security, Compliance, Finance, Operations, and other functions across OKX. About the Opportunity We are looking for an experienced Senior Analyst, Blockchain Investigations & Fraud Intelligence to join FIT. This is first and foremost a blockchain investigations role requiring significant hands-on expertise tracing digital assets across complex blockchain ecosystems. The successful candidate must be capable of independently conducting sophisticated on-chain investigations, developing and testing attribution hypotheses, identifying relationships among wallets and entities, following assets through multiple chains and protocols, and translating highly technical blockchain activity into clear investigative findings. Blockchain analysis is a specialized investigative discipline. Accordingly, we are seeking someone who understands not only how to operate commercial tracing platforms, but also the underlying blockchain architecture, transactional mechanics, attribution methodologies, clustering techniques, tracing limitations, and evidentiary considerations necessary to reach reliable conclusions. The role extends beyond on-chain analysis. The Senior Analyst will combine blockchain intelligence with enterprise data, system logs, transactional records, open-source information, and other evidence to support complex fraud and insider-risk investigations. The position will also provide analytical support to the Global Physical Security Team (PST) on selected protective-intelligence and threat-assessment matters. The Senior Analyst will directly manage one Analyst and will be expected to serve as a subject-matter resource for blockchain investigations and analytical methodology across FIT. What You’ll Be Doing Blockchain Investigations & Digital-Asset Intelligence Lead and conduct complex blockchain investigations and transaction tracing across multiple wallets, counterparties, entities, blockchains, tokens, protocols, and digital-asset ecosystems. Trace the origin, movement, conversion, layering, consolidation, and ultimate disposition of digital assets , including activity spanning multiple intermediaries and blockchain environments. Conduct investigations using tools such as Chainalysis Reactor , TRM Labs, Elliptic, or comparable blockchain intelligence platforms , while understanding the methodologies and limitations underlying platform-generated attribution. Analyze activity involving centralized exchanges, self-hosted wallets, decentralized exchanges, cross-chain bridges, mixers, privacy-enhancing technologies, DeFi protocols, smart contracts, stablecoins, NFTs, OTC activity, gambling platforms, and other relevant ecosystems . Trace assets across multiple chains and cross-chain transactions , identifying continuity of funds where technically supportable and clearly documenting assumptions or limitations where definitive attribution is not possible. Analyze transaction graphs, address relationships, behavioral patterns, clusters, counterparties, timing, amounts, transaction sequencing, and other relevant blockchain indicators. Understand and appropriately apply wallet-clustering and attribution methodologies , including the limitations and potential for false attribution associated with particular heuristics. Independently evaluate blockchain intelligence rather than relying solely on automated labels, risk scores, or vendor-generated attribution. Corroborate on-chain findings, where appropriate, with internal account records, KYC / KYB information, transaction data, access information, open-source intelligence, legal-process returns, or other available evidence . Identify relationships among blockchain addresses, OKX accounts, external counterparties, individuals, organizations, vendors, affiliates, and other relevant entities. Reconstruct complex transaction flows and create clear fund-flow diagrams, transaction maps, timelines, and visualizations that allow investigators and nontechnical stakeholders to understand the movement of assets. Analyze blockchain activity associated with suspected fraud , theft, account takeover, social engineering, insider activity, collusion, payment diversion, asset misappropriation, or other misconduct . Identify new wallets, counterparties, entities, or transactional patterns that may generate additional investigative leads. Clearly document the methodology, assumptions, confidence level, and limitations associated with blockchain findings. Distinguish between confirmed attribution , vendor attribution, analytical inference, behavioral similarity, and an unverified investigative hypothesis . Avoid presenting blockchain proximity, exposure, clustering, or a commercial risk score alone as proof of ownership, control, intent, or misconduct. Maintain current knowledge of emerging blockchain technologies, protocols, tracing methodologies, obfuscation techniques, fraud typologies, and developments affecting blockchain attribution or investigations . Fraud Investigations & Investigative Intelligence Conduct and support investigations involving internal and external fraud , employee misconduct, insider threats, vendor and third-party misconduct, collusion, conflicts of interest, asset misappropriation, unauthorized access, credential misuse, and other significant fraud risks . Combine blockchain findings with off-chain evidence to develop a comprehensive investigative picture rather than treating blockchain activity in isolation . Develop investigative hypotheses and objectively test those hypotheses against available evidence, including information that supports, contradicts, or fails to establish the hypothesis. Conduct timeline reconstruction, link analysis, relationship mapping, pattern analysis, clustering, and comparative analysis across individuals, accounts, wallets, devices, systems, transactions, and entities. Support investigations involving account takeover, impersonation, social engineering, business-email compromise, payment diversion, affiliate misconduct, and trusted-party abuse , particularly where a digital-asset, employee, vendor, or enterprise nexus exists. Analyze vendor, affiliate, partner, and other third-party activity for indicators of billing irregularities, undisclosed relationships, conflicts of interest, unusual payments, control circumvention, or other potential misconduct. Support investigative interviews by developing transaction timelines, identifying inconsistencies or information gaps, organizing evidence, and identifying appropriate areas for follow-up. Enterprise Data & Insider-Risk Analytics Analyze appropriately authorized information including system and access logs, authentication records, audit trails, customer-account activity, KYC / KYB information, transaction records, payment information, case-management records, vendor information, and relevant security telemetry . Correlate blockchain activity with enterprise data to identify possible connections among wallets, accounts, employees, customers, vendors, devices, transactions, and other relevant entities . Support investigations involving suspected unauthorized access, information misuse or exfiltration, credential compromise, control circumvention, unauthorized disclosure, or abuse of privileged access. Identify anomalous behavior or access patterns that may warrant additional investigation. Develop repeatable analytical methodologies capable of processing and correlating information from disparate systems. Use appropriate scripting, database querying, data visualization, and analytical technologies to efficiently analyze large or complex datasets. Investigative Reporting & Evidence Prepare clear, concise, and well-supported blockchain investigation reports, investigative memoranda, executive summaries, transaction-flow diagrams, link charts, timelines, and other analytical products . Explain complex blockchain concepts and transaction activity in plain language suitable for investigators, senior management, Legal, regulators, or law enforcement , as appropriate. Clearly distinguish among allegations, investigative leads, known facts, third-party attribution , analytical judgments, assumptions, information gaps, and substantiated conclusions . Ensure analytical conclusions are proportionate to the evidence and do not imply greater certainty than the underlying blockchain or other data supports. Maintain sufficient documentation to allow significant blockchain findings to be reviewed, reproduced, and understood by another qualified analyst . Preserve and handle investigative records in accordance with applicable evidence-preservation, chain-of-custody, confidentiality, privacy, access-control, and need-to-know requirements . Support appropriately authorized law-enforcement, regulatory, litigation, or legal matters in coordination with the appropriate company functions. Protective Intelligence & Physical Security Support Provide analytical support to PST on selected matters involving threats or concerning activity directed toward employees, executives, offices, events, or other company interests. Conduct authorized open-source, link, timeline, blockchain, financial, or other analytical research where relevant to understanding the identity, relationships, capabilities, or activity associated with a potential threat. Assist in evaluating available information relating to credibility, corroboration, intent, capability, access or opportunity, targeting, escalation, timing, and other relevant indicators . Prepare objective analytical products identifying known facts, analytical judgments, material information gaps, uncertainties, and appropriate confidence levels. Support PST’s assessment while preserving PST’s responsibility for protective-security decisions and operational response . Leadership & Development Directly manage and develop one Junior Analyst, including work allocation, coaching, technical development, quality assurance, and review of analytical work product . Serve as a FIT subject-matter resource for blockchain investigations and on-chain analytical methodology . Review blockchain analysis for technical accuracy, appropriate methodology, corroboration, reasonable attribution, and evidentiary support. Train and mentor junior personnel in transaction tracing, blockchain intelligence platforms, analytical reasoning, documentation, and investigative methodology. Help establish consistent standards for blockchain investigations, attribution , documentation, quality assurance, and reporting . Participate in time-sensitive investigations affecting a global organization, including occasional work outside normal business hours when required. What We Look For In You 7+ years of relevant investigative or analytical experience , with meaningful professional experience specifically conducting cryptocurrency or blockchain investigations . Demonstrated hands-on expertise independently conducting complex blockchain tracing investigations , rather than solely reviewing blockchain alerts or commercial risk scores. Advanced proficiency with one or more blockchain intelligence platforms such as Chainalysis Reactor , TRM Labs, Elliptic, or comparable technology . Strong understanding of how blockchain intelligence platforms derive attribution, clustering, exposure, and risk information—and the limitations associated with those methodologies . Demonstrated understanding of blockchain fundamentals, including wallets and addresses, transactions, UTXO and account-based models, tokens, smart contracts, block explorers, transaction fees, confirmations, and blockchain-specific transactional structures . Strong understanding of digital-asset ecosystems including centralized exchanges, self-hosted wallets, DeFi , DEXs, stablecoins, cross-chain bridges, mixers, privacy-enhancing mechanisms, and other relevant technologies . Demonstrated ability to trace assets across multiple chains, protocols, and intermediaries and recognize when technical limitations prevent definitive continuity or attribution. Experience conducting transaction-graph analysis, wallet attribution , clustering, counterparty analysis, transaction-flow reconstruction, and investigative link analysis . Ability to critically assess blockchain attribution and distinguish among technical facts, commercial vendor intelligence, analytical inference, and unsupported assumptions . Experience integrating blockchain intelligence with off-chain evidence and enterprise information to develop investigative conclusions. Strong knowledge of fraud-investigation principles including corroboration, source evaluation, evidence preservation, timeline development, hypothesis testing, and documentation. Experience analyzing large or complex datasets and familiarity with tools such as SQL , advanced Excel, Python, Splunk, or comparable data-analysis technologies . Ability to clearly explain technically complex blockchain findings to audiences that may have little or no blockchain expertise. Strong investigative writing skills and demonstrated ability to produce accurate, concise, and defensible analytical products. Sound judgment and ability to work effectively where evidence is incomplete, conflicting, or evolving. Demonstrated discretion when handling sensitive employee, customer, investigative, security, or company information. Demonstrated experience mentoring, supervising, or reviewing the work of junior analysts or investigators. Nice to Haves Experience conducting blockchain investigations within a cryptocurrency exchange, blockchain analytics company, law-enforcement agency, regulatory organization, financial institution, intelligence function, or investigative consulting environment . Experience investigating digital-asset matters involving fraud proceeds, theft, account compromise, insider activity, social engineering, asset tracing, or complex cross-chain movement . Experience supporting asset recovery, law-enforcement referrals, subpoenas/legal process, civil litigation, regulatory inquiries, or other matters requiring defensible blockchain analysis . Experience investigating insider threats, employee misconduct, unauthorized access, data exfiltration, credential compromise, vendor fraud, conflicts of interest, or trusted-party abuse. Experience with OSINT, digital forensics, enterprise logs, security telemetry, or identity and access-management information . Experience creating custom blockchain analytics through APIs, SQL , Python, blockchain explorers, node data, or other raw-data sources , rather than relying exclusively on commercial graphical interfaces. Relevant blockchain-specific training or certifications, including certifications associated with Chainalysis , TRM Labs, or comparable blockchain-analysis platforms . Professional certifications such as CFE, CFCS, CAMS, GIAC, or comparable investigative, fraud , financial-crime, cybersecurity, or intelligence credentials . What Success Looks Like Success in this role requires significantly more than knowing how to enter an address into a blockchain analytics platform. The successful candidate should be able to receive a complex transaction, wallet, allegation, or investigative question and independently determine how to trace the activity, which chains and protocols are involved, what the available data establishes, what additional information is needed, what attribution can reasonably be made, and where the technical or evidentiary limitations prevent a definitive conclusion . The individual should be able to move seamlessly between on-chain and off-chain evidence , recognizing that a blockchain transaction may establish movement of assets without necessarily establishing the identity, knowledge, intent, or conduct of the person behind a wallet. They should also be capable of converting a highly technical blockchain investigation into a concise and defensible explanation that an investigator, attorney, executive, regulator, or law-enforcement partner can understand and act upon. At the same time, this person must be sufficiently versatile to apply the same investigative discipline to internal fraud , insider risk, enterprise data, third-party misconduct, and protective-intelligence matters when required. The hierarchy is much clearer this way: blockchain investigations are the specialized core competency; fraud /investigative intelligence is the broader mission; enterprise analytics and PST support are complementary capabilities. That should materially improve the candidate pool versus a title that makes blockchain expertise appear interchangeable with general data analytics. OKX Statement: OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. The salary range for this position is $143,000 - $214,000 The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via OKX internal or external careers site. Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Задачи: — Участвовать во всем жизненном цикле разработки программного обеспечения; — Тесно сотрудничать с разработчиками и CTO для поставки качественных программных решений; — Отлаживать и устранять проблемы своевременно. Требования: — Минимум 4 года профессионального опыта в веб-разработке, предпочтительно с Typescript; — Опыт работы Fullstack Web3 разработчиком не менее 2 лет с языками программирования Solidity (подтвержденный опыт) и Rust; — Владение фронтенд-технологиями: Typescript, Javascript, React, NodeJS; — Владение бэкенд-технологиями: NodeJS; — Желателен опыт с Golang и Python; — Свободное владение английским языком (проходной уровень и сильный акцент не подходят); — Умение работать с клиентами; — Желателен опыт работы со смарт-контрактами и DeFi протоколами (curve, uniswap, AAVE и др.). Условия: — Полностью удаленная работа, только для резидентов стран с часовыми поясами США (EST-PST); — Конкурентная зарплата в зависимости от опыта и вклада; — Приоритет на оптимальный баланс работы и личной жизни. Процесс найма: — Заполнение анкеты; — Знакомство с HR; — Техническое интервью и техническая оценка; — Предложение о работе.

Location: San Francisco, CA | New York City, NY | Seattle, WA 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 role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale. This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet. Key responsibilities Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale Own end-to-end performance of our largest RL runs, from research code down to the hardware Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause Minimum qualifications Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack Preferred qualifications Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise Experience developing RL algorithms for language models Experience with scaling laws or other quantitative models of training efficiency Experience designing or modifying transformer architectures beyond standard configurations Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices Experience with C++ or Rust Representative projects Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel 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: $500,000 — $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. 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: San Francisco, CA | New York City, NY | Seattle, WA 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 role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea. As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience. Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs. Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing quickly. Key responsibilities Design, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment execution Find and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordination Build fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human intervention Design resource management and autoscaling so that compute follows demand as a run's needs shift Build observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected results Build automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safely Work with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminism Remove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you build Minimum qualifications Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go Experience designing, building, and operating large-scale distributed systems in production Deep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recovery Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and network Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally Strong written communication, including design documents and incident writeups Preferred qualifications Experience running ML training or inference infrastructure at scale Experience across several layers of the stack, such as scheduling, storage, networking, and orchestration Experience building schedulers, autoscalers, or resource management systems Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale Experience with high-performance networking, RDMA, or collective communication libraries Experience building observability or automated remediation for large fleets Experience with async Python frameworks such as Trio or asyncio Familiarity with reinforcement learning or large language model training workloads Representative projects Design a scheduler that places training, sampling, and environment work across a heterogeneous cluster while respecting network topology and failure domains Build a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost work Scale environment execution substantially without increasing tail latency for the training step Design an autoscaling policy that rebalances compute across components as a run's bottleneck shifts Build a diagnostics system that explains why a run's throughput dropped and proposes a fix Trace a rare data corruption bug across many services to a race condition in a recovery path, and redesign the path so the class of bug can't recur Design the operational interface for a run so that automated tools can safely diagnose and adjust it under human oversight 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: $500,000 — $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. 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.