Данные и ML вакансии - гибрид

Задачи: — Проектировать и запускать пакетные и потоковые конвейеры данных на Databricks и Apache Spark в AWS, включая загрузку из баз данных, API и потоков событий. — Формировать архитектуру lakehouse на S3 и Delta Lake с использованием медальонного слоя (Bronze / Silver / Gold) и оптимизацией хранения. — Работать напрямую с аналитиками, инженерами и продуктовыми командами для преобразования сырых данных в надежные и полезные бизнес-данные. — Вести проекты от согласования с заинтересованными сторонами и обзора дизайна до реализации, запуска и поддержки. — Создавать решения для самостоятельного доступа к данным для других команд, масштабируемые и расширяемые. — Строить и поддерживать масштабируемые ETL/ELT конвейеры с отказоустойчивостью и оптимизацией затрат. — Оптимизировать Spark задачи по размеру кластера, кэшированию, соединениям, шифлам, партиционированию и форматам хранения (Parquet, Delta). — Управлять рабочими процессами Databricks, заданиями, конфигурациями кластеров и инфраструктурой, включая политики безопасности и доступ. — Внедрять управление данными и контроль доступа с Unity Catalog, включая обработку персональных данных. Требования: — Практический опыт работы с Databricks и глубокое владение Apache Spark (PySpark и/или Scala). — Сильные навыки Python для обработки данных, а также SQL и Spark SQL для трансформаций и аналитики. — Опыт работы с Delta Lake и архитектурами lakehouse, включая оптимизацию производительности. — Подтвержденный опыт владения проектами, умение выявлять слабые места, собирать обратную связь и создавать масштабируемые решения. — Опыт написания чистого, тестируемого и поддерживаемого кода с использованием контроля версий на Git. — Опыт работы с инфраструктурой как кодом (Terraform или Ansible) является плюсом, но не обязателен. Условия: — Возможность создавать инновационные решения в финтехе Юго-Восточной Азии. — Опционы на акции с четырехлетним вестингом и 25% клиффом после первого года. — Гибридный формат работы с возможностью полностью удаленной работы для подходящих ролей, основные часы с 12:00 до 19:00 по манильскому времени (UTC+8). — Предоставление инструментов и оборудования компанией. — Офис в Bonifacio Global City, Манила. — Медицинская страховка с поддержкой для сотрудника и семьи. — Доступ к внутреннему специалисту по психическому здоровью. — 22 дня отпуска, государственные праздники Филиппин и 15 дней больничных. — 105 дней оплачиваемого декретного отпуска и до 14 дней оплачиваемого отпуска по уходу за ребенком для сотрудников, переезжающих в Манилу. — Поддержка при релокации для сотрудника и семьи, включая перелеты, проживание, визы, оформление документов и логистику. — Возмещение расходов на перевозку домашних животных. — Ежегодно до 2500 долларов США на перелеты домой. — Ежемесячные выплаты на детей для сотрудников, переезжающих в Манилу: 1000 долларов на ребенка до 2 лет и 500 долларов на ребенка от 2 до 18 лет. — Возможности для профессионального роста через внутренние встречи, внешние конференции, выступления и публикации. — Спонсируемые компанией поездки в Манилу для личных встреч с командой. — Регулярные командные выезды, спортивные мероприятия и события. — Возможность для высокоэффективных команд получить неделю отдыха на пляже в Юго-Восточной Азии.

Дивизион "Моделирование и исследование данных" отвечает за компетенцию Data Science в Блоке Сеть Продаж. Мы разрабатываем и внедряем end-to-end решения на основе AI в бизнес-процессы Сети Продаж Банка. Ищем человека, вместе с которым мы будем строить топовые AI решения, на основе архитектуры Claw-like & Ouroboros, которые будут в действительности приводить к Disrupt в реальных процессах. Задачи: • разрабатывать с нуля AI-агентов и микросервисы для AI-продуктов • исследовать и внедрять новейшие технологические решения в ИИ - Claw-like & Ouroboros • разрабатывать и дообучать модели машинного обучения (Classic ML, Deep Learning) • разрабатывать алгоритмы взаимодействия ML-моделей и сервисных модулей • принимать участие в разработке архитектуры и дизайна агентов и AI-сервисов • проектировать и разрабатывать backend-части AI-сервиса • принимать участие в оптимизации скорости работы AI-агентов и ML-моделей. Мы ожидаем: • законченное высшее образование • компетенции: Python, Classic ML, Deep Learning, LLM/Agents, MLOps • понимание принципов разработки, внедрения и сопровождения AI-решений • уметь писать хороший и понятный production-ready код на Python, хорошее знание алгоритмов, структур данных • опыт работы с реляционными и нереляционными базами данных • навыки работы с генеративными AI-моделями, опыт создания AI-агентов и использование их в работе будет преимуществом • опыт использования GigaChat, Kandinsky и аналогов в продуктах • инструментальное владение Ai для анализа, генерации и автоматизации • опыт построения отказоустойчивых высоконагруженных распределенных ML систем. Мы готовы предлагать: • график 5/2, офис на м. Кутузовская; • режим работы на выбор - офис или гибрид • достойный уровень вознаграждения (оклад + годовая премия) • возможность стать частью команды, реализующей программы цифровой трансформации банка - на основе передового опыта и инновационных идей • широкие возможности для профессионального развития: корпоративный университет и множество онлайн-программ обучения. Корпоративное обучение за счет компании • бесплатная подписка СберПрайм+, многочисленные скидки и бонусы от партнеров: СберМаркет, МегаМаркет, Самокат, Еаптека и др.

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

Задачи: — Полное владение датасетами безопасности: сбор, очистка, разметка, контроль качества, версионирование и подготовка к обучению и оценке моделей. — Перевод требований политики безопасности в четкие и последовательные критерии разметки и оценки, в тесном сотрудничестве со специалистами по политике. — Проектирование и управление процессами разметки, включая поиск, адаптацию и контроль внешних участников для обеспечения высокого качества. — Создание рабочих процессов оценки моделей в продакшене с использованием реальных данных для отслеживания производительности и выявления проблем. — Разработка легковесных пайплайнов и инструментов на Python и SQL для ускорения и воспроизводимости работы с данными, в партнерстве с ML-инженерами по требованиям к "готовности к обучению". Требования: — Опыт работы Data Scientist или аналогичной роли с реальными датасетами и ML-моделями. — Глубокое понимание качественного датасета: сбор, очистка, выборка, разметка, контроль качества, оценка. — Уверенное владение Python и SQL для анализа и практических рабочих процессов с данными. — Сильное критическое мышление и способность принимать решения в условиях неоднозначности, умение превращать сложные руководства в последовательные и масштабируемые решения. — Автономность и хорошие коммуникативные навыки, способность работать в междисциплинарной команде (политика, инженерия, исследования) в быстро меняющейся среде. Преимущества: — Опыт в AI safety, trust & safety, модерации контента или обнаружении мошенничества/злоупотреблений. — Опыт создания датасетов или фреймворков оценки, опыт с adversarial/safety-ориентированным ML. — Опыт управления процессами разметки с участием человека, версионирования датасетов или инструментов ML-пайплайнов. — Опыт внедрения руководств совместно с командами политики или юридическими отделами. Условия: — Работа в гибридном формате в Лондоне, тесное взаимодействие с командами AI Safety и Policy. — Равные возможности для всех кандидатов без дискриминации по различным признакам.

Mycar Digital — цифровой проект для автомобилистов: маркетплейс по продаже и покупке авто, автоматизация бизнес-процессов, автокредитование и дилерские центры. Формат работы: гибрид (Алматы, Казахстан).

Location: Foster City, CA Workplace: Hybrid Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role Join Replit's key teams across the company, such as AI Research, Strategic Finance, or the Office of the CEO, for a unique paid internship built for sharp quantitative and creative minds. You will work alongside our top executives, in addition to world-class engineers, designers, and finance team on some of the hardest problems in AI-native software creation and accelerating key areas of our business. We are creating a dedicated track for students with strong mathematical backgrounds because the problems we are solving sit at the intersection of deep math and applied AI, including agent reasoning, systems optimization, and improving how our models learn and perform at scale. Your work will directly shape how millions of users build software. You Will: - Contribute to real engineering problems that push the boundaries of AI-powered software creation - Collaborate with engineers, designers, and product managers on infrastructure that powers Replit's platform - Prototype novel approaches to problems in AI, systems, or tooling where mathematical rigor is the differentiator - Ship work that impacts millions of developers globally, in an environment where your ideas are heard and often implemented Required Skills and Experience: - Currently pursuing a Bachelor's, Master's, or PhD in Mathematics, Computer Science, Computer Engineering, Statistics, Physics, or a related quantitative field - At least one semester of schooling remaining after the internship - Demonstrated excellence in competitive mathematics such as IMO and IOI, quantitative research, or advanced coursework - Genuine curiosity about AI, agent systems, company building, or developer tooling - Extremely bullish on Replit and the future of AI-native software creation. We would love to see what you have built on the platform What We Value: - First-principles thinking: Ability to break down complex problems from the ground up and reason about them rigorously - Mathematical intuition: Strong grasp of the underlying math behind machine learning, optimization, or systems - Self-directed and autonomous: Capable of working independently while collaborating well with cross-functional teams - Strong communication: Ability to explain complex quantitative concepts to both technical and non-technical audiences - Curiosity and speed: Genuine excitement for hard problems and the drive to iterate quickly Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder   Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

Location: Kazakhstan; Kyrgyzstan; Uzbekistan Workplace: Hybrid Salmon is a technology-driven financial company building a banking and lending platform across Southeast Asia, starting in the Philippines. We combine global fintech expertise with deep local market knowledge to make financial services simple, accessible, and useful for millions of people across the region. 7M+ app downloads. 2M+ monthly active users. 7,000+ partner stores. US$310M+ raised from leading global investors. Manila-based, globally distributed, and hybrid-first — our team spans 45+ countries. If you want to solve complex problems at scale and impact how millions of people access and manage money, come build with us. Southeast Asia's fintech moment starts here. About the role: You'll build the analytics layer for Finance and Marketing & Growth — two areas that only get partial coverage today — and help shape how analytics engineering scales as the team and data volume grow. What you'll do: - Build the semantic layer that powers Finance's financial reporting, turning business logic into reliable, gold-layer datasets. - Build the analytics layer for Marketing & Growth, enabling self-service reporting on acquisition, funnels, and campaign performance. - Help define how analytics engineering scales across the team — supporting other analysts' data models and shaping technical standards. What you'll own: - Own the semantic layer for financial reporting, ensuring one consistent source of truth for business metrics. - Own the analytics layer for Marketing & Growth, from data modeling to self-service reporting. - Build and maintain dbt models (staging, intermediate, marts) with tests, documentation, and reusable macros. - Design scalable data models in Databricks silver and gold layers, applying dimensional modeling. - Support other analysts across the data team, optimizing self-built data models when performance or reliability issues come up. - Contribute to defining the strategy and standards for analytics engineering as it grows. What makes you a strong fit: - Strong SQL expertise, including complex queries, window functions, and query optimization on large datasets - Hands-on experience with dbt — models, macros, tests, documentation, and CI workflows - Strong dimensional modeling experience (3NF/Inmon, star schema, or anchor modeling) - Experience building semantic layers or maintaining a single source of truth for business metrics - Experience in banking or fintech is a plus, especially data modeling for financial products - Comfortable working in Git-based ELT workflows What we offer: Ownership and flexibility - Stock option plan — four-year vesting with a 25% cliff after the first year - Remote-first role, with collaboration hours from 10:00 Manila time (UTC+8) - Company-provided tools and equipment Health and time off - Medical insurance support for you and your family through co-funding or reimbursement, depending on your location and subject to policy limits - Access to an internal mental health support specialist - 22 vacation days, Philippine public holidays, and 15 sick days Growth and team experience - Opportunities to learn and share your expertise through internal expert meetups, external conferences, speaking opportunities, and industry publications - Company-sponsored trips to Manila to meet and work with your team in person - Regular team offsites, sports activities, and team events - High-performing teams can earn a dedicated beach house week in Southeast Asia We believe strong teams are built by people with different backgrounds, experiences, and points of view. Salmon is an equal opportunity employer, and we make hiring decisions based on skills, experience, and potential.

Задачи: - Развивать корпоративное хранилище данных: проектировать слои, схемы, таблицы и представления, оптимизировать структуру хранения; - Разрабатывать и сопровождать витрины данных для аналитики; - Проектировать и разрабатывать процессы загрузки данных из разнородных источников; - Настраивать потоки передачи данных между системами: интеграции, синхронизация, обработка ошибок; - Строить дашборды, алертинг и мониторинг; - Обеспечивать наблюдаемость пайплайнов: контроль качества данных, отслеживание задержек и сбоев загрузок; - Проводить аудит текущих решений, выявлять точки роста; - Собирать и уточнять требования к данным вместе с аналитиками и бизнес-заказчиками; - Участвовать в анализе данных: выявление зависимостей, построение логических связей, подготовка данных для аналитики. Требования: - 4+ года коммерческого опыта в роли Data Engineer; - Понимание принципов построения DWH (Kimball / Inmon, star schema, ETL/ELT); - Опыт построения систем мастер-данных; - Умение самостоятельно превратить размытую бизнес-задачу в модель данных и план работ. Будет плюсом: - Опыт ответственности за архитектуру хранилища или крупного домена целиком; - Внимание к качеству данных как отдельной задаче: проверки, SLA, договорённости с владельцами источников; - Опыт с ML и Computer Vision. Условия: - Комфортный офис в Mega Alma-Ata с паркингом и видом на горы; - Гибридный формат работы; - Официальное трудоустройство, медстраховка для вас и близких, компенсация фитнеса.

Location: San Francisco Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We’re hiring a Data Scientist to help build and scale our internal analytics capabilities. This is a foundational role where you’ll create dashboards, models and insights to power business and product teams alike. You’ll collect requirements, define key metrics, and deliver recommendations directly to stakeholders. You'll define what success looks like across a technical, usage-based platform and turn ambiguous questions into analyses, forecasts, and experiments that shape Baseten’s product and strategy. RESPONSIBILITIES - Build and maintain production-grade models and dashboards across multiple functions with a focus on accuracy, simplicity and user experience. - Define and instrument core metrics around capacity, availability, ROI, product adoption, revenue and costs. - Ingest and transform raw data using tools like dbt, Airbyte, and Databricks. - Partner with Engineering, Finance, Marketing, and Sales teams to understand goals and translate them into data solutions - Analyze product, operational and commercial data to identify opportunities, quantify tradeoffs, and influence product and GTM priorities. - Present findings and recommendations to technical and nontechnical audiences, including company leaders. REQUIREMENTS - 5+ years of experience in analytics, BI, or data engineering roles - Strong SQL skills with the ability to write complex queries for data transformation and analysis - Experience designing medallion data architectures, including raw, conformed, and business-ready models with testing, documentation, and lineage. - Excellent communication skills and a collaborative approach to working with cross-functional teams - Demonstrated ability to explain complex work and make clear recommendations to technical and business audiences. NICE TO HAVE - Experience with AI infrastructure, developer platforms, or inference. - Hands-on forecasting expertise, including ARIMA, Prophet, or comparable time-series methods, with disciplined backtesting, error analysis, and scenario planning. - Working knowledge of model-serving and training workflows and the infrastructure metrics that shape performance and cost. - Practical experimentation experience, including test design, power analysis and knowing when directional evidence is sufficient to act. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Задачи: — Проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы и автономных AI-агентов (планирование, function calling, мультиагентная оркестрация) — Дообучение и адаптация: SFT/RLHF/DPO, LoRA/QLoRA под бизнес-задачи; решать, когда нужно дообучение, а когда достаточно промпт-инжиниринга или RAG — Поддерживать культуру качества: внедрять продуктовые и ML-метрики (RAGAS, DeepEval, LLM-as-a-Judge), автоматизировать регрессионное тестирование — Писать чистый, документированный и тестируемый python-код, проектировать API-контракты для ML-компонент — Участвовать в код-ревью, формировать технические стандарты команды, работать напрямую с продуктом и стейкхолдерами Требования: — Опыт в DL/NLP от 4 лет, включая обучение и дообучение моделей на PyTorch, из них не менее года практического опыта с LLM — Практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain/LangGraph — Глубокое понимание архитектуры современных LLM: attention, MoE, RoPE, KV-cache, методы alignment (RLHF/DPO), принципы эффективного инференса — Сильная инженерная культура: Python (asyncio, pydantic, fastapi), чистый тестируемый код (pytest), асинхронные микросервисы; понимание Docker/K8s, CI/CD для ML — Оценка и мониторинг: уверенное владение инструментами LLMOps (Arize, LangSmith, W&B, Phoenix), опыт создания кастомных eval-пайплайнов — Работа с данными: опыт предобработки сложных данных для датасетов, знание SQL

Location: San Francisco, California Workplace: Hybrid WHO WE ARE Notion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. ABOUT THE ROLE: - You will apply your expertise in statistical inference and experimentation to understand the impact of product changes and drive decision-making. - You will explore ambiguous areas and conduct analysis to identify opportunities for the Growth org, helping shape the strategy and roadmap. - You'll partner closely with product, engineering, and marketing leaders at every step of the development process, driving forward data science and analytics work. - You'll work with your teams to determine north star and operational metrics, and build the foundational datasets and dashboards to monitor progress and make key decisions. - You'll influence the broader company by communicating your findings and driving change in our product and business. (Insights are useful. Impact is even better!) SKILLS YOU'LL NEED TO BRING: - You are comfortable with ambiguity and motivated by business results. - You have expertise in SQL and at least one scripting language (ideally Python or R). - You know how to use statistical inference and experimentation to drive actionable recommendations. - You are comfortable transforming raw data to build your own data sets if the measure you need doesn't exist yet. - You have a bias for using the right tools to get a job done with maximum efficiency. You have experience making tradeoffs between speed and accuracy. NICE TO HAVES: - You have worked at a fast-growing start-up. - You have experience at a B2B SaaS company. - You have a track record of acting as a thought partner to product, engineering, and marketing leaders, driving strategic decisions on these teams using data. - You have experience building predictive models, and you know how to evaluate their effectiveness. We hire talented and passionate people from a variety of backgrounds because we want our global employee base to represent the wide diversity of our customers. If you’re excited about a role but your past experience doesn’t align perfectly with every bullet point listed in the job description, we still encourage you to apply. If you’re a builder at heart, share our company values, and enthusiastic about making software toolmaking ubiquitous, we want to hear from you. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know. Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role’s scope and complexity, and the candidate’s experience and expertise, and may vary from the range provided below. For roles based in San Francisco or New York City, the estimated base salary range for this role is $145,000 – $233,000 per year. By clicking “Submit Application”, I understand and agree that Notion and its affiliates and subsidiaries will collect and process my information in accordance with Notion’s Global Recruiting Privacy Policy https://notion.notion.site/Notion-Global-Recruiting-Privacy-Policy-fc3eb4e829354a26a2bb6fd5e289b550. https://dev.notion.so/a3ce416456924276a59de91bca740028?pvs=21 #LI-Onsite A NOTE ON AI You don’t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement — when that’s the case, we'll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on. EQUAL OPPORTUNITY & ACCOMMODATIONS We hire talented people from a wide range of backgrounds. If you’re excited about this role but don’t meet every bullet, we still encourage you to apply. Notion is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Consistent with applicable law, we will consider for employment qualified applicants with arrest and conviction records. Notion provides reasonable accommodations during the application process; if you need one, please let your recruiter know. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.

Задачи: — Разрабатывать и поддерживать масштабируемые сервисы ML-моделей, оптимизируя их производительность и эффективность. — Проектировать инфраструктуру, обеспечивающую критически важные входные данные для алгоритма диспетчеризации, влияющего на опыт миллионов клиентов по всему миру. — Тесно сотрудничать с дата-сайентистами и инженерами для понимания их потребностей и создания масштабируемых решений. — Разрабатывать и улучшать инструменты ML-инжиниринга для разработки моделей, мониторинга, обслуживания и экспериментов. — Использовать облачную инфраструктуру Delivery Hero, построенную на современных технологиях и популярных облачных платформах, для создания высокодоступных систем. — Предлагать инновационные решения и новые технологии для поддержки новых кейсов. Требования: — Опыт ML-инжиниринга или MLOps не менее 4 лет, знакомство с инструментами для обслуживания моделей, обучения, экспериментов и инженерии признаков. — Опыт построения инфраструктуры для ML-пайплайнов с использованием современных инструментов и языков программирования, таких как Python. — Значительный опыт (от 3 лет) в проектировании и реализации различных моделей машинного обучения (регрессия, классификация, ансамбли деревьев) для решения бизнес-задач, с хорошей теоретической базой в статистике и теории вероятностей. — Опыт разработки приложений с нуля и создания быстрых прототипов для решения системных задач с использованием open-source инструментов. — Опыт технической оценки технологий и предложений решений на основе анализа плюсов и минусов. — Опыт глубокого анализа существующих систем для их улучшения. — Свободное владение английским языком, четкая устная и письменная коммуникация. — Желательно: опыт работы с ML-инструментами Metaflow, Airflow, MLflow, Argo Workflows, Cloud Notebooks. — Желательно: знание публичных облачных платформ (AWS, Google Cloud или аналогичных). — Желательно: опыт работы с CI/CD пайплайнами и инфраструктурой как код (Terraform или аналог). — Желательно: знание контейнеризации и оркестрации (Docker, Kubernetes, Helm). Условия: — Релокация в Берлин, Германия. — Гибридный формат работы: 2 дня в неделю в офисе на кампусе в Берлине. — 27 дней отпуска с дополнительными днями на 2-й и 3-й год работы. — Поддержка развития и карьерного роста: образовательный бюджет 1000 €, языковые курсы, поддержка родителей, доступ к Udemy Business. — Медицинские обследования, медитация, спортзал. — Программы поддержки сотрудников: план покупки акций, банк саббатикала, скидки на транспорт, страхование жизни и несчастных случаев, корпоративная пенсия. — Питание: цифровые и обычные ваучеры на еду. Компания Delivery Hero — одна из крупнейших технологических платформ Европы, работающая в 65 странах, штаб-квартира в Берлине, котируется на Франкфуртской бирже с 2017 года.

Задачи: — Разрабатывать AI-ассистентов, чат-ботов и AI-агентов. — Создавать многоэтапные AI-workflow. — Интегрировать OpenAI, Anthropic и другие LLM API. — Работать с context, memory, state и tool calling. — Разрабатывать AI-решения для Telegram, WhatsApp и Web. — Автоматизировать бизнес-процессы с помощью AI. — Разрабатывать и улучшать RAG-системы. — Работать с embeddings, semantic search, hybrid search и reranking. — Настраивать обработку, chunking и индексацию документов. — Работать с векторными, SQL- и NoSQL-базами данных. — Реализовывать цитирование и проверку источников ответов. — Разрабатывать backend на Python / FastAPI и создавать REST API. — Работать с Pydantic и асинхронным Python. — Интегрировать внешние API и базы данных. — Реализовывать retry, timeout, error handling и logging. — Контейнеризировать сервисы через Docker. — Оптимизировать промпты и системные инструкции. — Создавать тестовые сценарии и evals. — Выявлять галлюцинации и проблемы с контекстом. — Проводить регрессионное тестирование AI-функциональности. — Сравнивать модели по качеству, скорости и стоимости. — Контролировать token usage, latency, ошибки и расходы на LLM. — Анализировать реальные диалоги пользователей и улучшать AI-системы. Требования: — От 2 лет коммерческого опыта в AI Engineering, Backend, ML Engineering или смежной технической роли. — Уверенный Python. — Практический опыт работы с FastAPI. — Опыт работы с REST API, Pydantic и асинхронным Python. — Опыт работы с Docker и Git. — Практический опыт интеграции OpenAI, Anthropic или других LLM API. — Опыт работы с LangChain, LangGraph или аналогичными инструментами. — Понимание RAG, embeddings и semantic search. — Опыт разработки AI-ассистентов, чат-ботов или AI-агентов. — Опыт работы с SQL и/или NoSQL. — Понимание ограничений LLM: галлюцинации, context window, latency и стоимость. — Умение писать структурированный и поддерживаемый код. — Способность самостоятельно разбираться в задачах и предлагать технические решения. — Английский язык. Будет большим плюсом: — Qdrant, Pinecone, pgvector, Weaviate, Milvus или другие vector DB. — Production-ready RAG. — AI Agents и tool calling. — Structured Outputs, JSON Schema и Function Calling. — LLM evaluation / automated evals. — LangSmith, Langfuse, Arize Phoenix или аналогичные инструменты. — PostgreSQL, Redis, Elasticsearch / OpenSearch. — CI/CD. — AWS, GCP, Azure. — OCR и обработка документов. — Опыт разработки multi-tenant SaaS. — Понимание информационной безопасности и защиты данных. AI-инструменты в разработке: — Claude Code — Cursor — GitHub Copilot — Codex и аналоги Ожидания на первые 3 месяца: — Разобраться в существующей AI- и backend-архитектуре. — Самостоятельно развивать LLM-функциональность. — Реализовать минимум одну значимую AI-функцию от идеи до production. — Улучшить качество, стабильность, скорость или стоимость существующих AI-сценариев. — Настроить базовое тестирование и оценку качества AI-ответов. — Улучшить логирование и мониторинг ключевых AI-сценариев. Условия: — Полная или частичная занятость. — Формат: удалённо / гибрид. — График: 5/2 и другие варианты. — Рабочий день 8 часов. — Оформление по трудовому договору. — Остальные условия обсуждаются индивидуально с финальным кандидатом. — Работа над production-ready AI-системами, а не только демонстрационными решениями.

Задачи: -Планирование архитектуры приложений с использованием LLM в качестве основной технологии -Разработка ML-бэкендов и микросервисов для обеспечения функциональности и интеграции различных сервисов -Оптимизация текущих решений с акцентом на производительность и управление нагрузкой (onnx, openvino, tensorrt) Требования: -Опыт работы в роли MLE от 2-х лет -Уверенное знание Python и опыт разработки сетевых/асинхронных приложений -Широкая насмотренность в области LLM, RAG -Знание Docker, CI/CD, FastAPI и SQL Условия: -Возможна полная или частичная удалённая работа из России -Офисы в Москве, Краснодаре, Новосибирске

Location: London; Europe; Toronto Workplace: Hybrid WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role overview: North is Cohere’s AI workspace platform for enterprises: a secure, customizable environment where companies can use AI across their real workflows while maintaining control over sensitive data. North connects AI agents with workplace tools, applications, and business context, helping users delegate complex work, build automations, inspect outputs, and collaborate with AI in production environments. As North becomes more capable, one of the most important questions is also one of the hardest: how do we know whether the model is actually getting better for the workflows customers care about? This role is about being the voice of North inside modelling. You will build the evaluation systems, feedback loops, and applied modelling workflows that make sure model progress translates into better product outcomes for North users. You will work closely with North product teams, customer-facing teams, and modelling teams to define what “good” means across the product surface, turn real usage and product direction into high-quality evals, and use those evals to guide model selection, patches, and regular model updates. This is neither a pure research role nor a conventional product engineering role. It is a rigorous applied MLE role for someone who cares deeply about measurement, model behavior, and real-world product quality. You should be excited by the craft of building careful evals: evals that capture messy agentic workflows, reflect actual customer needs, resist superficial benchmark hacking, and provide useful signal for where the product and models need to go next. Please note: this team works closely across Europe and East Coast North America time zones. We are open to candidates who can collaborate effectively within those hours. Key responsibilities: - Own the eval strategy for North: define what we need to measure across agent workflows, tool use, enterprise knowledge work such as deep research, document creation or editing, and other human-AI interactions. - Build high-quality evals from the realities of the product: user feedback, production failures, privacy-preserving usage logs, internal dogfooding, customer needs, and forward-looking product goals. - Create systems that continuously turn what North is learning from users, customers, and feature teams into evals, so measurement keeps pace with the product rather than becoming a static benchmark. - Be the voice of North inside modelling: extract clear insight from eval results, customer-derived data and product context, then turn model failures, capability gaps, and North-specific needs into actionable recommendations for the central modelling teams. Qualifications: - You have improved LLM-powered, agent-powered, or AI-product systems through evals, feedback loops, data curation, prompting, model adaptation, or model selection. - You care deeply about evaluation as a craft: representative tasks, precise rubrics, clean data, failure analysis, regression tracking, and knowing when a metric is giving false confidence. - You have strong applied MLE judgment and can reason clearly about model behavior, eval validity, product outcomes, and production tradeoffs. - You are comfortable working close to users and product teams, while translating messy qualitative signals into measurement that other modelling teams can act on. - You are self-directed, practical, and motivated by open-ended problems where the right answer requires both technical depth and product understanding. - You care about making AI systems genuinely useful in real enterprise settings, not just better on abstract benchmarks. If some of the above does not line up perfectly with your experience, we still encourage you to apply. Working Location: This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role. FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them. - Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled. - Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem. - Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself. - Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships. - Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve. - Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself. - Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk. REQUIREMENTS - Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack - Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems - Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems - Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product - Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership - Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it - Willingness to respond to customers outside regular working hours and participate in an on-call rotation - Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads WHAT YOU'LL BRING We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following: - Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE) - Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads - A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang - The ability to profile and optimize GPU workloads, in training or serving - Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX - Operational depth - running on-call, leading incident response, debugging distributed systems under pressure BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco Bay Area, USA Workplace: Hybrid WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands.   We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. ABOUT THE ROLE We're looking for exceptional Machine Learning Engineers focused on Ads to help take Higgsfield's advertising platform to the next level. You'll work at the intersection of large-scale machine learning, generative AI, and advertising systems—building the models and infrastructure that determine how creative is generated, ranked, optimized, and ultimately performs. This role is for someone who understands ads systems deeply and is equally strong in modern generative AI. You should be comfortable moving across ranking and recommendation, targeting and optimization, prompt engineering, post-training, and production ML systems. You'll help define what an AI-native advertising platform looks like from the ground up. WHAT YOU'LL DO: - Build and improve ML systems powering advertising products, including ranking, recommendation, targeting, prediction, and optimization. - Develop models that improve ad creative quality, relevance, personalization, and performance at scale. - Build systems that connect generative models with real-world advertising performance signals, creating feedback loops that continuously improve model outputs. - Apply prompt engineering and post-training techniques to improve generative models for advertising and creative use cases. - Work on fine-tuning, preference optimization, evaluation, and other techniques for adapting foundation models to specific creative and advertising objectives. - Design and run experiments across creative generation, ranking, targeting, and delivery to understand what drives advertiser performance. - Build production ML systems that operate reliably at significant scale, from experimentation through inference and serving. - Work closely with Product, Research, Engineering, and GTM teams to turn advances in generative AI into products advertisers can use. REQUIREMENTS - Deep experience building machine learning systems for advertising. - Strong understanding of ads systems, including areas such as ranking, recommendation, targeting, bidding, conversion prediction, creative optimization, or measurement. - Hands-on experience with LLMs, multimodal models, or generative AI systems. - Strong experience with prompt engineering and model evaluation. - Experience with post-training, including techniques such as supervised fine-tuning, preference optimization, reinforcement learning, or related approaches. - Strong software engineering fundamentals and experience shipping production ML systems. - Ability to operate across research and engineering: you can experiment quickly, identify what works, and turn it into a scalable production system. - High agency. - Working English. NICE TO HAVE - Experience building ads, ranking, or recommendation systems at a major consumer, social, search, or advertising platform. - Experience with generative video, image, or multimodal models. - Experience using downstream signals such as CTR, CVR, ROAS, engagement, or retention to train or optimize ML systems. - Experience with large-scale model training, inference optimization, or distributed ML infrastructure. - Experience building AI systems that generate or optimize advertising creative. COMPENSATION & BENEFITS • We offer a competitive and thoughtfully structured compensation package designed to align with impact, experience, and long-term growth. • Base Salary: The anticipated salary range for this role is $165 - $230k, depending on experience, skills, scope, and location. • Equity: In addition to cash compensation, employees are eligible to participate in the company’s stock option program and share in Higgsfield’s long-term growth. • • Benefits: We offer a comprehensive benefits package designed to support employees professionally and personally • The opportunity to work on ambitious AI products alongside a highly experienced, fast-moving, and international team. • Significant ownership, direct impact, and opportunities for professional growth as the company scales.   This is a hybrid role based in the San Francisco Bay Area. Team members are expected to work from our San Francisco office three full days per week, with the remaining days worked remotely and are expected to be available during agreed working hours and to maintain sufficient overlap with the relevant team’s time zone. We value in-person collaboration, active communication, responsiveness, and close partnership across teams. Higgsfield AI is an equal opportunity employer. We are committed to building a diverse and inclusive team and do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual