Вакансии

Задачи: — Подготавливать данные и выстраивать пайплайны обучения. — Проводить эксперименты с архитектурами, методами и режимами обучения. — Обучать модели с нуля и адаптировать их под целевые задачи. — Оценивать качество моделей, анализировать ошибки и улучшать результаты. Требования: — Практический опыт обучения языковых моделей: от подготовки данных до оценки результата. — Опыт аналогичных проектов либо адаптации готовых моделей методами, выходящими за рамки SFT. — Уверенное владение Python и PyTorch. — Понимание принципов обучения трансформеров и опыт работы с GPU. Будет плюсом: опыт распределённого обучения, оптимизации памяти и скорости обучения, работы с моделями для кода и вызова инструментов. Проекты для заказчиков под NDA — подробности обсудим лично.

Location: Москва Наша команда занимается созданием следующего поколения интеллектуальных систем для анализа рисков. Среди наших проектов: * разработка NLP моделей на основе LLM, создание sota AI-агентов и мультиагентных систем для операционных и ESG-рисков * создание transformer-based моделей для решения банковских задач на финансовых данных; моделирование финансовых рынков с помощью машинного обучения * разработка классических ML-моделей для процесса кредитования, которые являются ядром бизнеса Сбера и ключевым драйвером прибыли. и сейчас наша команда в поиске талантливых стажеров трех направлений: Data Scientist, LLM-researcher, и Quant-Risks в Управление риск-моделирования. Обязанности * разработка моделей для автоматизации и оптимизации кредитных процессов * сбор, анализ и подготовка данных для моделирования * написание поддерживаемого, читаемого кода; участие в обсуждениях технических подходов * помощь в опромышливании результатов исследований * презентация результатов исследований бизнес-заказчиками. Требования * статус студента бакалавриата/магистратуры на момент трудоустройства * в области компьютерных наук, математики или другой релевантной области * знания в области теории вероятностей и математической статистики * отличные навыки программирования на Python, SQL. * знание библиотек Numpy, Pandas, Scikit-learn, XGBoost/LightGBM/CatBoost, Transformers, Pytorch * опыт разработки моделей машинного обучения (классический стек, NLP, DL). **Будет плюсом:** опыт работы с LLM, c большими данными (PySpark). Условия * формат работы: офис в г. Москва, рядом с м. Кутузовский пр. * гибкий график и возможность совмещать с учебой: от 30 до 40 часов в неделю * возможность заниматься спортом в одном из спортзалов банка * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Наша команда занимается созданием следующего поколения интеллектуальных систем для анализа рисков. Среди наших проектов: * разработка NLP моделей на основе LLM, создание sota AI-агентов и мультиагентных систем для операционных и ESG-рисков * создание transformer-based моделей для решения банковских задач на финансовых данных; моделирование финансовых рынков с помощью машинного обучения * разработка классических ML-моделей для процесса кредитования, которые являются ядром бизнеса Сбера и ключевым драйвером прибыли. и сейчас наша команда в поиске талантливых стажеров трех направлений: Data Scientist, LLM-researcher, и Quant-Risks в Управление риск-моделирования. Обязанности * разработка моделей ценообразования и расчета риск-метрик для финансовых инструментов, а также подходов к управлению рыночными рисками * развитие передовых методов ИИ для финансов: генеративное моделирование мультимодальных данных (диффузия, VAE, LLM и др.), обучение с подкреплением * обозревать статьи и придумывать новые SoTA подходы для решения банковских задач на финансовых данных. Требования * статус студента бакалавриата/магистратуры на момент трудоустройства, образование с математическим уклоном * понимание финансовой математики: знания в области теории вероятностей и случайных процессов, математической статистики и эконометрики * свободное владение Python (NumPy, Pandas, PyTorch) * базовые знание ML/DL * знание классических методов ML, Deep Learning * знание современных генеративных моделей / RL-алгоритмы * знание архитектуры и принципов LLM. Условия * формат работы: офис в г. Москва, рядом с м. Кутузовский пр. * гибкий график и возможность совмещать с учебой: от 30 до 40 часов в неделю * возможность заниматься спортом в одном из спортзалов банка * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Наша команда занимается созданием следующего поколения интеллектуальных систем для анализа рисков. Среди наших проектов: * разработка NLP моделей на основе LLM, создание sota AI-агентов и мультиагентных систем для операционных и ESG-рисков * создание transformer-based моделей для решения банковских задач на финансовых данных; моделирование финансовых рынков с помощью машинного обучения * разработка классических ML-моделей для процесса кредитования, которые являются ядром бизнеса Сбера и ключевым драйвером прибыли. и сейчас наша команда в поиске талантливых стажеров трех направлений: Data Scientist, LLM-researcher, и Quant-Risks в Управление риск-моделирования. Обязанности \- разработка PoC и MVP AI-агентов на основе LLM с использованием фреймворков для работы с LLM, таких как langchain/langgraph \- адаптация и обучение LLM Сбера с использованием внутренних и внешних данных (Prompt Tuning, LoRA, SFT, GRPO) \- применение RL подхода для улучшения reasoning способностей модели. Требования * статус студента бакалавриата/магистратуры на момент трудоустройства * знание статистики и ML/DL алгоритмов * знание Python и ML-стэка (PyTorch, Scikit-learn, Pandas) * понимание как устроен современный NLP (токенизация, обработка данных, эмбеддинги, pre-train модели и тд) * опыт проектирования и обучения нейросетевых моделей (от CNN/RNN до трансформеров) * опыт построения RAG (классический RAG, GraphRAG, Agentic RAG) * знание классических алгоритмов (two pointers, бинарный поиск) и структур данных (куча, стэк, бинарное дерево) * уверенный английский язык для чтения и анализа научных статей и документации Условия * формат работы: офис в г. Москва, рядом с м. Кутузовский пр. * гибкий график и возможность совмещать с учебой: от 30 до 40 часов в неделю * возможность заниматься спортом в одном из спортзалов банка * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Требования: — Уверенный опыт работы с LLM/SLM (Few-shot learning, prompt engineering) и BERT (классификация сценариев и интентов); — Практический опыт finetuning LLM (PEFT, LoRA/QLoRA) под узкие прикладные задачи; — Уверенный дебаг LLM: умение быстро понять, почему модель ответила не так, и исправить это промптом, гиперпараметром или файнтюном; — Опыт дообучения и оптимизации TTS-моделей; — Опыт работы и интеграции современных ASR-систем (в частности, GigaAMv2); — Опыт работы как с графовыми/сценарийными диалоговыми движками, так и с LLM-агентами; — Высокая скорость диагностики проблем в инференсе, локализации багов и принятия инженерных решений; — Уверенное владение Python (PyTorch, huggingface, фреймворки инференса) и Go; — Опыт работы с оптимизацией инференса под low-latency (vLLM, TensorRT-LLM, Triton Inference Server, ONNX Runtime); — Практика работы со потоковым аудио в реальном времени (streaming ASR/TTS, gRPC-контракты); — GoLang: - Базовое понимание по дизайну сервисов; - Понимание SOLID, DRY и KISS паттернов; - Знание типизации в Go, хотя бы базовые типы (строки, числа, слайсы и мапы); - Знание примитивов синхронизации и конкуренции (как синхронизировать горутины, как работать с каналами); - Знание указателей и работы с ними; - Observability; - Будет плюсом: опыт работы с GRPC; - Будет плюсом: умение настроить инфраструктуру, k8s/inventory, знания работы TCP. Задачи: — Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог; — Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.); — Оперативное принятие архитектурных решений и их самостоятельная реализация в коде; — Поддержка и быстрая отладка few-shot классификации сценариев на базе Qwen и классификаторов на BERT; — Учёт краевых случаев, исправление ошибочных срабатываний интентов; — При необходимости — finetuning локальных LLM (извлечение сущностей, удерживание инструкций); — Интеграция и поддержка ASR на базе GigaAMv2 (отладка качества распознавания, работы VAD и передачи текста дальше по пайплайну); — Дообучение и настройка инференса собственных и опенсорсных TTS и систем клонирования голоса для достижения нужного качества звучания и скорости работы.

Требования: — Критически важно: оформленный военный билет или приписное свидетельство. — LLM/NLP: LLM/SLM, prompt engineering, few-shot, BERT, PEFT, LoRA/QLoRA. — Voice AI: дообучение TTS, ASR (GigaAMv2), VAD, streaming ASR/TTS. — Python: PyTorch, HuggingFace, inference frameworks. — Low-latency: vLLM, TensorRT-LLM, Triton или ONNX Runtime. — Go: сервисная архитектура, SOLID/DRY/KISS, типы, goroutines/channels, указатели, observability. — Будет плюсом: gRPC. — Kubernetes и инфраструктура. — Глубокое понимание TCP/networking. Условия: — Локация: удалённо, РФ. — Гражданство РФ. — Полная занятость (Full-time).

Задачи: — проектирование, разработка и внедрение решений для масштабного инференса AI моделей. Требования: — навыки работы с машинным обучением, Diffuser, Git, C#, NCCL, PyTorch, Python, TensorFlow, NumPy; — опыт с agile разработкой; — умение оптимизировать код и проводить код-ревью; — знание алгоритмов, отладки и научных библиотек pandas, scipy. Условия: — работа в офисе в Софии, Болгария; — стажировка с возможностью получить опыт в AI и ML разработке.

Location: Los Angeles, USA As a Staff Machine Learning Engineer on an unpublished R&D game project, you will own the strategy, design, and implementation of ML capabilities. You will work closely with developers, designers, and data scientists to understand their needs, and will be hands-on in conceptualizing, planning, implementing, and maintaining ML-driven solutions. We’re looking for someone who is passionate about enabling great player experiences, thrives in cross-disciplinary collaboration, stays current on the latest ML techniques, and can apply them pragmatically to challenging product problems. You will report to the Engineering Manager within the R&D game project. Responsibilities: Work closely with developers, designers, and data scientists to identify and define improvement opportunities Refine modeling and feature strategy for using techniques such as reinforcement learning Establish pipeline architecture patterns in partnership with central teams; build reusable training and evaluation pipelines that can be adapted to Riot-wide use cases Drive the application of ML/RL systems to improve decision-making and adaptation Establish research-to-production practices; balance cutting-edge techniques with production reliability Establish evaluation practices in partnership with data scientists and analytics partners; guide experiment design for measuring model impact and set technical evaluation standards Define reliability standards in partnership with design and engineering Advance and strengthen engineering best practices, including code quality, automated testing, and maintainable architecture Mentor engineers and partner disciplines through documentation, reviews, and technical design guidance Identify technical risks and bottlenecks, proposing pragmatic solutions to keep projects moving forward Required Qualifications: 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles, ideally within real-time or interactive environments Experience with reinforcement learning, including applying RL to practical production problems Experience in production environments with interacting models, feedback loops, or systems where model behavior has downstream consequences beyond a single prediction Proficiency with modern ML/data science libraries and frameworks such as PyTorch, TensorFlow, pandas, etc. Experience collaborating regularly with teammates across multiple disciplines Strong communication skills across technical and non-technical audiences B.S. in Computer Science or equivalent experience Proficiency in Python or similar programming languages Desired Qualifications: Experience with multi-agent reinforcement learning Experience in developer tooling Experience implementing framework-level UI systems Experience in infrastructure and MLOps, such as ML cloud services, containers, and model telemetry Experience working in a AAA game engine (e.g. Unity, Unreal) Proficiency in C++ For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role! Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Location: Remote - Canada Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: Innodata is building a team of Prompt Engineers to leverage large language models (LLMs) to automate and optimize data annotation and human evaluation workflows. In this role, you will design and implement effective prompt strategies that improve accuracy, localization, and cultural alignment in data labeling and translation processes. Working closely with Product, Data Science, Operations, and client stakeholders, you will translate business requirements into scalable AI-driven solutions. You will identify automation opportunities, develop prompt-based workflows, and continuously measure and refine performance to ensure high standards of quality and reliability. This position offers the opportunity to directly impact efficiency, scalability and innovation for a leading global technology partner. What You’ll Own: Collaborate with data scientists, linguists, and localization experts to ensure accuracy and cultural relevance. Prototype and validate AI models to demonstrate initial feasibility, potential impact, and overall effectiveness. Design, develop, and implement prompts for data labeling and localization processes within software applications. Understand the current components of the software stack, use cases and problems and iterate on solutions leveraging a solid knowledge of data structures, data formats, and data modeling. Conduct user testing and feedback analysis to optimize prompt design for data accuracy and linguistic consistency. Analyze model performance using key performance indicators (KPIs) and metrics, ensuring that AI models meet customer acceptance criteria and deliver high-quality outputs. Communicate technical findings and solution strategies to both technical and non-technical stakeholders, including presenting model performance and actionable insights in a clear, accessible manner. Collaborate on data pipelines and workflows that integrate LLMs into automated systems, enhancing both the efficiency and effectiveness of data annotation tasks. Create guidelines and training materials for prompt usage in data labeling and localization projects. Stay informed on data labeling and localization industry trends and tools to enhance prompt engineering techniques. You’ll Thrive in This Role If You Have: 2 years of prompt engineering / LLM fine-tuning, or related AI/ML roles. Familiarity with tools/platforms for annotation and human-in-the-loop workflows (e.g., Labelbox). Experience designing and automating data annotation workflows. Knowledge of data annotation and the challenges of scaling human-in-the-loop workflows. Familiarity with cloud platforms, containerization, and model deployment. Deep understanding of LLMs (e.g. transformer-based architectures). Demonstrated experience programmatically using LLMs to automate data labeling, classification, localization and annotation tasks. Strong expertise in Python for NLU, for data processing & transformation, and for statistical analysis. Familiarity with JSON, Javascript or XML. Experience with popular frameworks and libraries, including TensorFlow, PyTorch, Jupyter, and other relevant AI/ML tools. Familiarity with APIs and platforms for working with LLMs (e.g., OpenAI, Hugging Face, etc.). Knowledge of localization best practices and cultural nuances for different languages and regions. Strong understanding of LLM evaluation metrics and the ability to assess model reliability, bias, and generalizability. Experience working with data pipelines, automation tools, and integrating models into production systems to ensure scalable, reliable solutions. The expected salary range for this position is $80,000 – $90,000 CAD per year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Задачи: — Вести разработку и проектирование систем компьютерного зрения для биометрии (атрибуты лица, детекция, качество и распознавание). — Проводить тщательный анализ справедливости и бенчмаркинг биометрических моделей на различных наборах данных и условиях эксплуатации. — Архитектурировать, обучать и оптимизировать модели с использованием PyTorch, Tensorflow и/или JAX. — Владеть и развивать сквозные ML-пайплайны от сбора данных до деплоя. — Проектировать автоматизированные пайплайны (Airflow) для сбора и очистки данных. — Курировать сбалансированные обучающие наборы и генерировать синтетические данные для устранения проблем с качеством и разнообразием. — Обеспечивать производство: отвечать за путь модели до продакшена. — Оптимизировать модели для низкой задержки вывода (квантование, дистилляция, TensorRT/ONNX) и управлять деплоем на AWS. — Наставлять ML-инженеров, проводить код- и дизайн-ревью, продвигать технические лучшие практики в команде компьютерного зрения. Требования: — Опыт работы в индустрии машинного обучения не менее 5 лет, из них минимум 3 года в биометрии или анализе лиц. — Глубокие знания в области компьютерного зрения и биометрии, особенно в распознавании лиц. — Понимание источников алгоритмического смещения в компьютерном зрении и практический опыт измерения и смягчения разного воздействия. — Экспертное владение Python и библиотеками для машинного обучения и компьютерного зрения (Pillow, OpenCV, PyTorch и др.). — Умение писать чистый, модульный, готовый к продакшену код. — Опыт проектирования сквозных ML-пайплайнов и работы с оркестраторами рабочих процессов, такими как Airflow. — Практический опыт масштабирования обучающих задач на мульти-GPU кластерах и деплоя сервисов на AWS (SageMaker, EC2, EKS). Желательно: — Публикации в CVPR, ICCV, ECCV или FG по темам распознавания лиц, оценки качества изображений или справедливости. — Опыт работы с векторными базами данных (Milvus, Faiss) и алгоритмами приближенного поиска ближайших соседей. — Знания в области приватности, безопасности и соответствия в биометрических системах. — Опыт портирования моделей на edge-устройства или мобильные платформы с использованием CoreML, LiteRT, TFLite. — Опыт генерации синтетических лиц с помощью GAN или диффузионных моделей. — Сильные коммуникативные навыки. Условия: — Полная занятость. — Удалённая работа из Индии. О компании: Jumio — B2B технологическая компания, специализирующаяся на борьбе с онлайн-мошенничеством, отмыванием денег и финансовыми преступлениями с помощью AI, биометрии, машинного обучения и автоматизации. Компания является лидером в области онлайн-верификации личности, eKYC и AML решений, работает с клиентами по всему миру в различных отраслях. Политика конфиденциальности: Личные данные кандидатов используются только в рамках процессов подачи заявки, рекрутинга и найма в Jumio, согласно их Applicant Privacy Notice.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Team Localization is where the map meets the moment. Our localization team builds the living geospatial world model and the centimeter-level Visual Positioning System (VPS) that lets people and robots know exactly where they are, from any camera, almost anywhere on Earth. That system is learned, not hand-built. Our localization stack has moved from classical geometry to visual models trained on billions of posed images — models that have to be accurate to the centimeter, robust to lighting and seasons and clutter, and fast enough to run in production for every query. This team sits at the junction of research and production: we take what works in a paper, make it work on our data, and make it work for customers. About the Role We're hiring an AI Engineer, Computer Vision, to contribute to the deep learning models at the heart of our localization system. Working alongside experienced researchers and engineers, you'll help train and iterate on the visual models — feature extractors, matchers, pose regressors, and retrieval models — that turn a single image into a precise position in the world. This is a full-loop environment. You'll get exposure to the entire pipeline: the data the model learns on, training, evaluation, and the service that runs it in production. You'll be surrounded by people who care deeply about correctness — where a loss curve going down is not the same as a system getting better — and you'll grow into that rigor quickly. What You'll Do - Train and Evaluate Models — Implement, train, and evaluate visual localization models in PyTorch: local feature extraction and matching, image retrieval, pose regression, and scene-coordinate regression. Iterate on architectures, loss functions, and training loops to improve accuracy on real production captures. - Build Data Pipelines — Contribute to high-throughput ingestion and preprocessing pipelines that turn raw imagery, poses, and 3D point clouds into training-ready datasets, including curation, sampling, and augmentation. - Run Experiments at Scale — Run training jobs across GPU environments on our 30-billion-image corpus, working with senior engineers and ML Infrastructure to keep experiments reproducible and results interpretable. - Extend the Benchmark — Help build and maintain the evaluation suite for localization — pose accuracy, recall across lighting and season changes, and latency under production load — so results are comparable across model versions. - Support Production Deployment — Work with senior engineers to take models from checkpoint toward serving: export, inference optimization, and monitoring once live. Learn what it means to own a model end-to-end, not just hand off a checkpoint. - Bridge Research and Product — Work with the Research team and Real-World Test Lab to understand how field failures translate into training data and benchmark improvements. What You'll Bring - Experience training deep learning models for computer vision tasks in PyTorch — through coursework, research, internships, or early industry work. - Foundational understanding of at least one of: visual localization, learned feature matching, image retrieval, pose estimation, SLAM, or structure-from-motion, and the 3D geometry underneath them (camera models, epipolar geometry, pose parameterizations). - Comfort working with large image or 3D datasets — preprocessing, curation, and understanding how data quality affects model behavior. - Familiarity with GPU-based training and the ability to read and reason about training runs — identifying whether a bottleneck is in data loading, compute, or model architecture. - Strong Python skills and a habit of writing code that others can rerun and build on. - A bachelor's or master's degree in computer science, computer vision, robotics, or a related field, or equivalent practical experience. Nice to Have - Published or contributed to work on learned localization, visual place recognition, scene-coordinate regression, or vision foundation models (CVPR, ECCV, ICCV, NeurIPS, 3DV, or equivalent). - Experience with large-scale self-supervised or multi-view pretraining, or fine-tuning vision foundation models for geometric tasks. - Exposure to inference optimization for edge or mobile hardware (TensorRT, ONNX, CoreML, or similar). - Experience with petabyte-scale spatial data: point clouds, meshes, posed image collections, or geospatial indexing. - Background in robotics, augmented reality, 3D reconstruction, digital twins, or autonomous systems. - Prior industry or research internship experience shipping or contributing to a production CV system. Compensation & Benefits The expected salary range for this role is $165,600 - $221,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco, CA office on a hybrid schedule with a minimum of 3 days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed — you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Ищем ML-инженера с сильным практическим опытом работы с LLM: fine-tuning, post-training, evaluation и inference. Важно: это не позиция классического ML Engineer / Data Scientist. Нужен кандидат, который самостоятельно обучал и адаптировал LLM и понимает полный цикл — от данных и обучения до evaluation и deployment. Задачи: - Разработка и улучшение пайплайна LLM post-training: SFT, preference optimization, Offline / Online RL; - Улучшение instruction following и построение системы benchmarking / evaluation; - Обучение LoRA / QLoRA-адаптеров под различные домены и задачи; - Работа с reward models / verifiers и reward functions для RL; - Разработка RAG-систем: retrieval, reranking, context construction и evaluation; - Обучение и интеграция tool use / function calling / structured outputs; - Оптимизация inference и serving моделей с использованием vLLM / SGLang; Обязательные требования: - Практический опыт fine-tuning / post-training LLM; - Опыт с SFT и preference optimization: DPO / IPO / ORPO / SimPO или аналогами; - Опыт или хорошее практическое понимание Online RL для LLM: GRPO / GSPO или аналогичных методов; - Понимание reward models, preference data и LLM evaluation; - Уверенное владение Python, PyTorch, Hugging Face Transformers; - Опыт с TRL и/или verl; - Опыт с LoRA / QLoRA / PEFT; - Опыт inference / serving через vLLM и/или SGLang; - Опыт разработки RAG, tool use или structured generation; - Понимание архитектуры современных LLM и особенностей их обучения и inference; - Опыт работы с GPU, distributed training и Linux / Docker / Git; - Умение самостоятельно ставить эксперименты, выбирать метрики и анализировать результаты. Будет плюсом: - Continual / domain pre-training; - Reasoning RL; - Multilingual LLM или code generation; - FSDP / DeepSpeed / Megatron-LM; - Quantization и оптимизация inference.

Задачи: — Разрабатывать человекоподобных чат-ботов, которые не только ведут естественный диалог, но и инициируют и выполняют пользовательские сценарии внутри продукта — Участвовать в архитектуре и разработке интеллектуальной системы перевода, сохраняющей смысл, тон и стиль — Проводить эксперименты и внедрять ML/NLP-функции в продакшен с высокой стабильностью и измеримыми результатами — Работать с командами продукта и инженерии для обеспечения быстрой, точной и надежной интеграции ML-компонентов с бэкенд-сервисами — Отвечать за полный жизненный цикл ML-функций: сбор данных, эксперименты, обучение моделей, деплой, мониторинг и улучшение качества Требования: — Опыт разработки на Python и поддержки production-сервисов от 3 лет — Опыт веб/API разработки: REST, версионирование, логирование, наблюдаемость — Уверенные навыки Pandas, NumPy и SQL — Опыт решения реальных ML-задач (NLP/CV) в продакшен-проектах от 1 года — Опыт с TensorFlow, Keras и/или Hugging Face — Понимание практик деплоя и мониторинга моделей — Знание GIT, HTTP(s), JSON/XML и базовых сетевых протоколов (TCP/IP) — Понимание основ SDLC, code review и CI/CD пайплайнов — Знакомство с архитектурами на базе LLM: prompt engineering, RAG/fine-tuning, интеграция с бэкендом — Опыт создания интеллектуальных чат-ботов, выполняющих реальные действия в продукте, а не только FAQ или поддерживающих ботов Плюсы: — PyTorch, SciPy, OpenCV, Matplotlib, LangChain или LlamaIndex — Docker, NoSQL или векторные базы данных (Weaviate, PGVector, Milvus) — Базовые знания JavaScript для интеграции ML-сервисов с фронтендом — Генерация изображений на основе диффузии (Flux, LoRA, оптимизация инференса) — Автоматизированные фреймворки оценки LLM, включая офлайн/онлайн тестирование и A/B тесты — Опыт работы с мультимодальными моделями (текст↔изображение, vision-language) Условия: — Удалённая работа — Зарплата от $10,000 gross — 2 недели оплачиваемого отпуска в год — Оплачиваемые больничные — Профессиональные курсы за счёт компании — График работы: воскресенье–четверг, 10:00–19:00 по израильскому времени

Location: United Kingdom; Poland; Portugal; France; Spain; Germany; Italy; The Netherlands Workplace: Remote Recognised on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads. The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla. We’re on a mission to transform how companies use data. Come be a part of our journey! ABOUT THE TEAM The AI/ML Engineering team builds and operates ClickHouse's AI and machine learning products end-to-end. This includes the Agentic Data Stack, AI Functions, chDB, the in-Console copilot, and the AI/ML partnerships that distribute them — together with the shared components and expertise that let every other ClickHouse team ship AI in the surfaces they own. Our team is looking for highly skilled and experienced software engineers to join us, who will be responsible for designing, building, and operating the products that make ClickHouse the platform of choice for agents and data scientists.   WHAT YOU WILL DO - Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform. - API Architecture: Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services. - Ecosystem Integrations: Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools. - Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation. - Oncall: Participate in the daytime oncall rotation along with the rest of the team. - ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products. WHAT YOU WILL BRING ALONG - 5+ years of software engineering experience in production environments - Exposure to working directly with AI/ML technologies - Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases - A high level of ownership and can drive features from concept to production with minimal supervision - Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders - Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions Nice to have - Familiarity with cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment - Understanding of database systems and data processing pipelines, with ClickHouse experience being a significant plus - Experience with embedding-based systems (retrieval, ranking, recommenders) and the representation-learning ideas underneath them PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Location: New York; Detroit; Canada; Austin; Phoenix; Los Angeles; San Francisco; Dallas Workplace: Remote Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads. The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla. We’re on a mission to transform how companies use data. Come be a part of our journey! ABOUT THE TEAM The AI/ML Engineering team builds and operates ClickHouse's AI and machine learning products end-to-end. This includes the Agentic Data Stack, AI Functions, chDB, the in-Console copilot, and the AI/ML partnerships that distribute them — together with the shared components and expertise that let every other ClickHouse team ship AI in the surfaces they own. Our team is looking for highly skilled and experienced software engineers to join us, who will be responsible for designing, building, and operating the products that make ClickHouse the platform of choice for agents and data scientists. WHAT YOU WILL DO - Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform. - API Architecture: Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services. - Ecosystem Integrations: Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools. - Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation. - Oncall: Participate in the daytime oncall rotation along with the rest of the team. - ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products. WHAT YOU WILL BRING ALONG - 5+ years of software engineering experience in production environments - Exposure to working directly with AI/ML technologies - Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases - A high level of ownership and can drive features from concept to production with minimal supervision - Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders - Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions Nice to have - Familiarity with cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment - Understanding of database systems and data processing pipelines, with ClickHouse experience being a significant plus - Experience with embedding-based systems (retrieval, ranking, recommenders) and the representation-learning ideas underneath them PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Задачи: — Руководить командой NLP-инженеров и backend-разработчиков — Планировать и приоритизировать задачи, проводить ревью и контролировать качество решений — Определять архитектуру и технологический стек LLM/NLP-разработки — Вести ключевой проект направления — банковскую платформу парсинга документов — Формулировать технические требования и декомпозировать задачи — Вести технический бэклог и приоритизировать запросы от нескольких заказчиков — Контролировать реализацию решений на всех этапах разработки — Взаимодействовать с бизнес-заказчиками и смежными IT-командами — Развивать экспертизу команды, организовывать обмен знаниями и формировать лучшие практики — Заниматься наставничеством и профессиональным развитием сотрудников — Участвовать в выборе, интеграции и оптимизации ML-, OCR-, LLM- и VLM-моделей — Проектировать масштабируемые решения для промышленной эксплуатации моделей Требования: — От пяти лет коммерческого опыта в NLP, LLM или backend-разработке на Python — Не менее трёх лет опыта в роли Tech Lead — Практический опыт управления инженерной командой и технической частью продукта — Глубокое понимание ML-алгоритмов, OCR-моделей, архитектуры LLM и VLM-моделей — Уверенное владение Python — Практический опыт работы с PyTorch, Hugging Face, sentence-transformers и FastAPI — Опыт интеграции и оптимизации моделей Qwen, DeepSeek, Llama, Mistral или аналогичных — Понимание принципов оптимизации inference — Знание подходов к масштабированию, кэшированию и распределённому inference — Опыт взаимодействия с бизнес-заказчиками и смежными IT-командами — Умение формировать и вести технический бэклог — Навыки сбора, декомпозиции и приоритизации требований от нескольких заказчиков Будет плюсом: — Опыт интеграции LLM с корпоративными системами и BI-платформами — Знание инструментов мониторинга LLM по показателям latency, quality и cost — Опыт работы с библиотеками Docling и Kreuzberg — Опыт проектирования и разработки высоконагруженных систем

Location: European Union About Toloka At Toloka AI we create data that powers leading GenAI models and innovations. We work with frontier labs, big tech, renowned AI startups, enterprises and non-profit research organizations worldwide. We use a combination of Experts + Crowd + Tech Platform to teach AI models to reason and evaluate their efficacy and safety. We have experts in more than 50 different domains—from doctors and lawyers to physicists and engineers—and boast one of the most diverse global crowds, representing ove r 100 countries and speaking 40+ languages. We are a well-funded startup with an enviable portfolio of clients including Anthropic, Amazon, Microsoft, Poolside, Recraft, and Shopify. Recently, we secured strategic investment led by Bezos Expeditions and Nebius Group with participation from Mikhail Parakhin, CTO of Shopify and board advisor to leading GenAI companies, who now serves as our Chairman of the Board. Our remote-first team is globally distributed around the world : USA, UK, the Netherlands, Serbia , and more. About the Team We are the ML team inside Toloka — we build the machine-learning products that power the platform itself, so every project running on Toloka is faster, cheaper, and more reliable. A few examples of what we own: Enterprise post-training- we are helping real companies by providing them small LLMs that beat frontier models in quality at a fraction of the cost. Off-the-shelf post-training - we are conducting research on datasets we deliver to clients, showing that training on these datasets will improve their performance. Fine-tuning and RL — adapting frontier and open-source models to Toloka's tasks to hit the right quality at the right cost. Evaluation, benchmarking, cost modeling, and model selection across providers. LLM QA — the core technology behind Toloka's automated quality-check mechanism. Every annotation flowing through Self-Service is reviewed by an LLM agent we design, train, and operate. We own the full chain. The same team designs the ML solution, ships it to production, keeps it running 24/7, analyzes the results coming back from real projects, and feeds that signal into the next iteration. No hand-off between research, engineering, and operations — it's all us. About the Position As a Principal ML Researcher, you will define the overarching technical vision, research strategy, and architecture for Toloka’s core ML and post-training stack. In this high-impact role, you will bridge frontier AI research and large-scale platform engineering. You will lead technical strategy across greenfield post-training paradigms (such as GRPO, process/outcome reward modeling, and RLAIF), architect resilient automated evaluation ecosystems, and set the standard for how foundation models are adapted and served on our platform. As a technical authority, you will mentor senior ML engineers, collaborate directly with executive leadership, and represent Toloka in the broader AI research community. What you’ll do Drive Technical Strategy: Define the multi-quarter strategy and architecture for Toloka’s post-training stack, ensuring our fine-tuning, RL, and evaluation capabilities stay ahead of industry trends. Architect Greenfield Post-Training & RL Pipelines: Spearhead the transition beyond basic SFT into advanced RL frameworks (GRPO, PPO/DPO, reward modeling, Process Reward Models, and RLAIF) to power platform-wide LLM alignment and agent behavior. Pioneer Next-Gen Evaluation Harnesses: Design and calibrate bulletproof automated evaluation ecosystems, including human-aligned LLM-as-a-judge frameworks, dynamic benchmarking suites, and regression control setups. Advance Distillation & Inference Optimization: Lead research initiatives on model distillation, quantization, context compression (e.g., gisting), and speculative decoding to optimize latency-cost trade-offs at platform scale. Shape Agentic Platform Capabilities: Architect autonomous guiding agents and tool-use workflows, establishing foundational frameworks for multi-step reasoning, self-correction, and evaluation-driven feedback loops. Technical Leadership & Mentorship: Elevate the engineering and research bar across the team through architectural design reviews, hands-on mentoring of Senior ML Researchers, and establishing best practices for reproducible ML R&D. Cross-Functional & Community Influence: Partner with Product and Engineering directors to translate complex client challenges into scalable platform architecture; author high-impact research write-ups, blog posts, and external tech talks. What we're looking for 6+ years in ML engineering or applied research, with 3+ years of proven track record leading LLM post-training, fine-tuning, or alignment initiatives at scale. Deep theoretical and practical mastery of LLM alignment: SFT, LoRA/PEFT, RLHF/RLAIF (GRPO, DPO, PPO), reward modeling, and reasoning-oriented post-training. Mastery of LLM Evaluation & Calibration: Proven history of building evaluation pipelines from scratch, addressing LLM-as-a-judge biases, and tightly calibrating automated evals against human gold standards. Expert Distributed Training & Systems Engineering: Strong proficiency in Python, PyTorch, distributed training frameworks (Deepspeed, Megatron, FSDP), and high-performance inference engines (vLLM, TensorRT-LLM). Architectural Vision & Product Leadership: Demonstrated ability to map ambiguous technical challenges into robust platform features, balancing state-of-the-art research with production reliability and cost constraints. Technical Authority: Proven experience acting as a technical leader, mentoring senior researchers/engineers, and influencing cross-functional roadmaps. Language: Fluent spoken and written English (C1), with clear communication skills to articulate complex technical concepts to both internal teams and external clients/community. What we can offer You will be part of an international, dynamic environment that drives innovation and sets new standards in the AI and technology sector. Competitive compensation package including base salary, bonus, and ESOP. Paid PTO and benefits will vary depending on location. We offer a full remote or hybrid model (if you are based in NL or Serbia). IT setup and home office allowances. Equal Opportunity Employer: Toloka is committed to providing equal opportunity and fostering an inclusive environment. We welcome applications from all qualified individuals and do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, gender identity, age, marital status, veteran status, disability, or any other characteristic protected by applicable law. Selection decisions are made based on qualifications, merit, and business need. [Important Notice] Scam Alert Regarding Fake Job Postings It has come to our attention that an individual or group is fraudulently impersonating Toloka to post fake jobs and solicit personal information from applicants. Please be aware: Official Communication: Our recruiting team will only contact you from an official " toloka.ai " email address. We will NEVER use Gmail, Yahoo, Tolokainc, toloka.inc, or other personal or seemingly business email accounts. Our Process: We will never ask for your bank account details, credit card number, or any fees as part of the application or interview process. Official Listings: All legitimate job openings are posted on our official careers page: https://toloka.ai/careers#job-list What to do: If you see a suspicious job posting or have been contacted by someone you suspect is a scammer, please do not provide any personal information. Instead, report the incident to us directly at security@toloka.ai and report the profile/post to LinkedIn.We are taking this matter very seriously and are working with the appropriate parties to resolve it. Thank you for your vigilance! To learn how we collect, use, disclose, and store personal data, check out our Privacy Notice .

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 We're looking for an Engineering Manager to lead part of our Inference Performance team. This team makes the world's most demanding AI workloads run faster and more efficiently on GPUs. You'll manage and grow a team of inference performance engineers working across the inference engine and runtime: kernels, scheduling, batching, KV-cache management, speculative decoding and prefill/decode disaggregation. This is a hands-on technical leadership role. You'll set direction, unblock hard problems and earn the team's trust by going deep on GPU performance, while also hiring, developing and supporting the people doing the work. Your team's output directly affects how fast our customers' models run and how efficiently we serve them. The team is scaling quickly, so you'll help shape how it is structured as it grows. EXAMPLE INITIATIVES Your team will work on these types of projects as part of our Inference Runtime team: - Agentic inference optimization: 50-90% faster engines https://www.baseten.co/blog/agentic-inference-optimization-faster-than-sota/ - Agentic Kernels in Production https://www.baseten.co/blog/agentic-kernels-in-production/ - Live draft model training for speculative decoding https://www.baseten.co/blog/live-draft-model-training-for-speculative-decoding/ - The Baseten Inference Stack https://www.baseten.co/resources/guide/the-baseten-inference-stack/ RESPONSIBILITIES - Lead, mentor and grow a team of inference performance engineers through regular 1:1s, clear feedback, career development and performance reviews. - Hire top GPU and inference engineering talent, and build a strong, collaborative team culture as the runtime team scales. - Own the technical roadmap and execution for runtime performance work, balancing customer needs, new model launches and long-term platform investments. - Stay close to the technical work. Review designs, guide profiling and optimization efforts, and help the team reason from first principles about where time and memory go. - Drive the productionization of inference techniques such as quantization, speculative decoding, KV-cache reuse, chunked prefill and custom scheduling. - Turn performance wins into measurable outcomes: tokens per GPU-hour, utilization, latency and cost. - Help the team bring up and tune new model architectures on new hardware quickly, often in the same week they're released. - Partner with Infrastructure, Inference Platform, Kernels, Model APIs and customer-facing teams to set priorities, coordinate launches and ship wins. - Set high standards for engineering quality, benchmarking, operational excellence and incident response. REQUIREMENTS - Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or a related field. - Experience managing engineers, including hiring, mentoring, giving feedback and running performance reviews. - Experience leading or closely supporting GPU optimization teams in training, inference or recommendation systems. - Strong technical depth in GPU workloads, with a solid understanding of GPU architecture and performance tradeoffs. - Familiarity with ML libraries such as PyTorch, TensorRT or TensorRT-LLM. - A track record of driving roadmaps and shipping complex technical projects with a team. - Clear written and verbal communication, including the ability to align stakeholders across teams. NICE TO HAVE - Familiarity with inference engines such as vLLM, SGLang or TensorRT-LLM. - Experience with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching) in production. - Experience with GPU kernels (CUDA, Triton, CUTLASS, or similar). - Experience scaling a team through rapid growth at a startup. - A background as a hands-on performance or systems engineer before moving into management. 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).