Вакансии

Location: Москва Мы — команда, которая занимается обучением LLM, VLM и CV моделей. Создаем нативные омнимодальные архитектуры, новые способы кодирования и совместного обучения в доменах изображений, аудио, видео и т.д. Наши основные направления: обучение LLM и VLM с нуля, разработка новых архитектур, Computer Vision research, создание opensource инструментов, сбор и подготовка данных, деплой моделей для MVP. Работаем на кластере с GPU уровня H100. **Ищем** Edge AI / ML Deployment инженера, который проведёт модель через весь путь от RnD-прототипа до prod-ready решения: оптимизация инференса, квантование и запуск на Edge-девайсах, масштабирование на кластерах. Обязанности * подготовка RnD-технологий к prod-ready решению; * оптимизация моделей и кода инференса для масштабирования на кластерах; * подготовка и запуск моделей на Edge-девайсах (Nvidia Jetson, Rockchip, Amlogic Khadas). Требования * опыт работы с Nvidia Jetson (Orin, Xavier, Nano); * уверенный C++ (для инференса) + Python (для скриптов и подготовки); * умение делать квантование моделей из PyTorch-весов (FP32, FP16, FP8, FP4), квантование (INT8, INT4), QAT и калибровка моделей; * Linux, CMake, опыт кросс-компиляции под ARM. **Будет плюсом:** * опыт работы с NPU от Rockchip (RK3588, RK3568, RV1126) / Amlogic (A311D, C308X, Khadas); * знание API Nvidia CUDA Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы * ежегодный пересмотр зарплаты, годовая премия от 3х окладов * большой спортзал и зоны отдыха * система обучения для профессионального и карьерного развития * расширенный полис ДМС с первого дня работы и страхование для семьи * программа ипотеки для сотрудников со скидкой -1/3 от текущей ставки * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Руководить командой ML-инженеров; Участвовать в найме и развитии команды;

Location: Hong Kong or Remote Nex is on a mission to help families rediscover the joy of movement. Created by parents for parents, Nex combines technology and play to deliver fun, social, and interactive experiences powered by natural body motion, encouraging kids and adults to move more, play more, and have fun together. Nex Playground, the company’s award-winning active play system, is purpose-built to get families moving year-round, with safety and privacy as core considerations in its intentional design. It is certified kidSAFE+ COPPA compliant and built to support healthy, active play for all ages and abilities. Nex Playground features a growing library of 50+ experiences, including motion and dance games, fitness and educational experiences, and Nex Originals. Content includes collaborations with partners like Hasbro, Sesame Workshop, and NBCUniversal. Nex has been recognized by Fast Company’s Most Innovative Companies, TIME’s Best Inventions, and Parents’ Best Entertainment System for Families, and has earned Red Dot, IDEA, and Core77 international design awards. We encourage you to explore Have Fun and Is Motion Gaming Back? , as they offer a deeper look into our culture, values, and explain how our approach to motion gaming differs from previous generations. Nex employees may be expected to collaborate with colleagues across different time zones. Depending on the role and location, occasional travel may also be required for company events, external meetings, and other business-related activities. Important Notice: ⚠️ To protect yourself from recruitment scams, please note that Nex communicates through official company emails ending in @nex.inc . If you receive a suspicious email or message, please do not respond and report it immediately to us. Location: Hong Kong or Remote Type: Full Time The Role As a ML Researcher at Nex, you will develop new machine learning models and algorithms that push the boundaries of computational perception and interaction on Nex Playground. You will join a small, deeply technical team that combines research and engineering to solve complex problems in sensing, understanding, and multimodal interaction. The ML Research role emphasizes rapid experimentation, exploring new ideas and methods to expand what our platform can sense, understand, and respond to. You will work within the ML Research group, collaborating closely with ML Engineers who build the infrastructure that accelerates your research. This role is ideal for researchers who want to see their work directly impact product capabilities while maintaining a focus on cutting-edge innovation. The Mindset You are driven by curiosity and technical discovery. You see research as a systematic process of exploration and validation, not just theoretical work. You balance scientific rigor with practical impact, knowing that the best research solves real problems. You thrive in a team that values experimentation velocity and measurable technical improvement. What You’ll Do Develop novel ML models and algorithms for computational perception and interaction Design and execute rapid experiments to validate new ideas and methods Explore advancements in computer vision, audio processing, sensor fusion, or related domains Collaborate with ML Engineers to integrate research outcomes into training pipelines and production systems Measure and track experimentation velocity, the number and quality of validated experiments per quarter Contribute to research planning and help define the technical roadmap alongside the Engineering Manager and Tech Lead Document research findings and communicate technical progress to the broader team Must Have 2+ years of hands-on ML research experience in industry, academia, or research labs Demonstrable track record of designing and conducting ML experiments from hypothesis to validation Proficiency in Python for ML research and experimentation Deep expertise with PyTorch or TensorFlow for model development Experience training and evaluating ML models on real datasets Understanding of model evaluation metrics, experimental design, and statistical validation Familiarity with data preprocessing, augmentation, and management for ML workflows Experience presenting research findings to technical audiences Nice To Have Expertise in real-time inference, model optimization, or efficient architectures Experience with self-supervised learning, few-shot learning, or foundation models Background in multimodal learning combining vision, audio, and sensor data Contributions to open-source ML projects or released research code Experience collaborating with engineers to productionize research outcomes Familiarity with ML Engineering practices: training pipelines, experiment tracking, MLOps Background in edge computing, on-device ML, or resource-constrained environments Experience with sensing technologies: cameras, microphones, IMUs, or haptic systems Knowledge of privacy-preserving ML, federated learning, or on-device data processing We Offer Competitive compensation package. Flexible working hours and vacation policy. Product-driven culture that treasures talents and individual growth. Front-row seat and hands-on experience with cutting edge technologies in the evolving gaming field Nex is located in San Jose, California, USA and Hong Kong . Learn more about us at https://www.nexplayground.com/about . We encourage applications even if you don’t meet more than 50% of the requirements — we believe that experience comes in many forms! Nex is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, gender identity, and sexual orientation), national origin, ancestry, age, physical or mental disability, medical condition, genetic information, marital status, military or veteran status, or any other characteristic protected by applicable law. This policy applies to all individuals at every stage of the employment relationship, including all current and prospective employees, and covers all employment decisions including recruitment, hiring, job assignment, promotion, compensation, benefits, training, discipline and termination. We are committed to providing a workplace free from discrimination and harassment and to fostering an inclusive environment for all employees.

Location: Brussels, Belgium We currently have a vacancy for a Data Scientist – Text mining / NLP fluent in English, to offer his/her services as an expert who will be based in Brussels, Belgium. The work will be carried out either in the company’s premises or on site at customer premises. In the context of the first assignment, the successful candidate will be integrated in the Development team of the company that will closely cooperate with a major client’s IT team on site and off site. Your tasks Design, implement and optimise advanced AI, NLP, and ML models. Use LLMs, RAG frameworks, and other state-of-the-art approaches; Create methods for tokenisation, part-of-speech tagging, named entity recognition, classification, clustering and other text mining-related tasks; Fine-tune pre-trained models on domain-specific tasks; Conduct thorough research and stay updated on the latest trends and advancements in NLP, ML, and AI technologies; Develop and maintain robust, scalable, and efficient code using Python; Collaborate with cross-functional teams to integrate AI/ML solutions into existing products and services; Perform rigorous analysis and experimentation to improve model accuracy, efficiency, and scalability; Participate in peer reviews and contribute to the continuous improvement of AI solutions; Contribute to the design and implementation of ML application architecture and its solution stack; Develop comprehensive reports and visualisations to communicate insights and findings to stakeholders. Requirements Bachelor degree in IT with focus on Statistics, Data Science, Machine Learning combined with 12 years of relevant working experience ; Experience in Machine Learning and Natural Language Processing; Experience with AI-powered code assistants (e.g., Amazon Kiro, Github Copilot), staying updated with advancements in AI-driven code technologies; Experience with A/B testing and experimental design of ML models; Experience with pre-trained models and LLMs like GPT, and other Transformer-based architectures; Experience with tools like Matplotlib and Seaborn for creating data visualisations; Experience with deep learning frameworks for complex model architecture such as TensorFlow or PyTorch; Proficient in continuous code delivery and unit testing; Excellent knowledge of Python and libraries (e.g. Pandas, SpaCy, NLTK, Hugging Face); Good knowledge of SQL tooling (Oracle, PostgreSQL); Knowledge of NoSQL databases (Elasticsearch, MongoDB); Knowledge of architectural design of scalable ML solutions such as model servers, GPU resource optimization; Knowledge in one of the following areas: predictive (forecasting, recommendation), prescriptive (simulation), topic detection, plagiarism detection, trends/anomalies detection in datasets, recommendation systems; Understanding of bias in ML applications and bias mitigation techniques; Strong understanding of linguistics and text processing techniques; Good knowledge of European Commission grant management ecosystem (eGrants) would be an asset; Excellent command of the English language. Benefits If you are seeking a career in an exciting and dynamic company, where you will offer your services as part of a team of a major European Institution, operating in an international, multilingual and multicultural environment where you can expect real chances to make a difference, please send us your detailed CV in English, quoting reference (158834/11/2026). We offer a competitive remuneration (either on contract basis or remuneration with full benefits package), based on qualifications and experience. All applications will be treated as confidential. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading Software, Information and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Stockholm, London, Nicosia, Hong-Kong, Valetta, etc). The company employs over 600 engineers, IT experts and consultants (around 3% PhD, 36% MSc and 53% BSc or equivalent). We design and develop software applications using state-of-the-art technology. The group generates annual revenues in the range of EURO 40 million, with an EBITDA in the range of 20%. The value of our contract portfolio exceeds EURO 250 million. EUROPEAN DYNAMICS is a renowned supplier of IT services to government institutions, multinational corporations, public administrations and multinational companies, research and academic institutes. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, color, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion. EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published in www.eurodyn.com/privacy . By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorise ED to process your personal data for the purposes of the company's recruitment opportunities, in line to the Policy. Furthermore, when providing your data, it is up to you to explicitly consent that your data can be assessed for future job openings, for as long as you do not withdraw such consent. If you do not consent, we will not be able to consider the data you provide to us for future job openings.

Location: Warsaw, Poland; Madrid, Spain; Tbilisi, Georgia; Sofia, Bulgaria; Yerevan, Armenia; Podgorica, Montenegro CloudLinux is a global remote-first company. We are driven by our principles: do the right thing, employees first, we are remote first, and we deliver high-volume, low-cost Linux infrastructure and security products that help companies to increase the efficiency of their operations. Every person on our team supports each other and does what we can to ensure we all are successful. Check out our website for more information: https://cloudlinux.com/ Imunify360 is CloudLinux's security suite for Linux web servers: real-time and background malware scanning, automated cleanup, a WAF, runtime PHP protection (Proactive Defense), a database malware scanner, and IP reputation. It runs on hosting providers' shared, VPS, and dedicated servers, so new web-malware campaigns land on our fleet early. We sit on one of the largest labeled corpora of live web-server malware in the industry: files, verdicts, cleanup outcomes, and analyst decisions collected from production hosting servers every day. It already drives our signatures, heuristics, and ML models, and there is far more to get out of it. We are looking for a Senior ML Engineer who will turn that data into detection that ships — classifiers, LLM-assisted triage and generated signatures — and keep exploring new ML/LLM methods against it, measured by the product's false-negative and false-positive rates, not by notebook accuracy. Our dataset focuses on web malware — PHP, JavaScript, HTML phishing kits, infected WordPress and Joomla plugins and themes, database injections — with the context around each file. What you'll work with: Malware samples from production servers, with verdicts and analyst decisions Years of signatures and detection rules Customer-reported false negatives and false positives Runtime behavior of code on live sites Web-attack traffic File metadata and how files spread across servers Cleanup outcomes, including what came back What you will do: The goal is 10x more malware detected at the same or lower false-positive rate. Hand-written rules and faster pipelines will not get there; models trained on our data might. Decide which files on a server deserve a closer look, within tight CPU and memory budgets Tell malicious code from benign at fleet scale and a sensible cost per file Generate detection signatures automatically from new campaigns Catch malware that only looks malicious in context, such as phishing pages and script injections Route each file to the right detection engine Make cleanup precise so sites stay clean afterwards Prove each model improves detection before it ships How we work: You get an experimentation track, GPUs, and a direct line to leadership; in return, you bring numbers, not opinions. Remote-first, async across UTC−5 to UTC+8. Decisions are written down (ADR-Lite) and public by default. Hardware follows evidence: Each training direction starts with dedicated GPUs on our own bare metal; leadership has committed to fund more when the case is made with data. Agentic engineering: Claude and Codex are the default tools, wired to GitLab, Jenkins, Redash, Jira, and Slite through MCP servers. We look at how engineers work with agents — context, specificity, review quality — not only at output. Close to ground truth: You work daily with malware analysts, detection and infrastructure engineers, and the rest of our ML team. Ship gates: No model or build goes out if it raises false positives or false negatives on our past-error sets. Blameless post-mortems within 24 hours. What you'll deliver: You own the path from raw samples to a model in production, and the numbers that prove it helped. First month: A first model trained on production data and measured against today's engines — suspicious-file triage or a metadata model. The evaluation harness in place: splits by time and campaign so families don't leak, and a regression set from past FPs and FNs. By month 3: Several models in production: some in shadow mode building evidence, some live and producing real detections, all gated on the past-error regression set. A training setup that keeps its GPUs busy. A written proposal (ADR) for the detection cascade, with cost per file and hardware needs. By month 12: Models are a main source of new detections, reported as share of new detections and FN-rate trend. Retraining on fresh analyst and FN labels on a fixed cadence. Generated signatures inside the analyst workflow. Two or three parallel training directions that other engineers can run with your tooling. Requirements What you bring: 5+ years building ML systems that ran in production, including 2+ in an adversarial domain: security, abuse, fraud, or spam. Y ou have trained and fine-tuned transformer models yourself — data preparation, tokenizing code, LoRA/QLoRA or full fine-tunes, distillation, quantization — not only called hosted APIs. Classic ML depth: gradient boosting, linear models on sparse features, calibration, class imbalance, and judgement on when these beat an LLM. Evaluation discipline: leakage-free splits by time and family, precision at a fixed false-positive budget, regression sets, and an honest account of why production will differ from offline. Inference cost engineering: you have measured throughput and latency and made a model fit a CPU or memory budget (ONNX, llama.cpp, vLLM, int8/int4). Strong Python, PyTorch and the Hugging Face stack; SQL on large analytical tables (ClickHouse or similar). Familiarity with PHP or JavaScript , or willingness to pick it up working with our malware analysts. You use AI coding agents daily and can show how. English at upper-Intermediate (B2) level or higher , with particularly strong written communication for async work. It is a strong plus if you have: Web malware, webshells, phishing kits or WordPress security experience. Models over code: AST or token features, code LLMs, graph models on program structure. Malware clustering and similarity analysis. Multi-GPU training on your own bare metal; MLflow or Weights & Biases; dataset versioning. Go or Rust, for on-server inference. Published work or talks on ML for security. Benefits What's in it for you? A focus on professional development. Interesting and challenging projects. Fully remote work with flexible working hours, that allows you to schedule your day and work from any location worldwide. Paid 24 days of vacation per year, 10 days of national holidays, and unlimited sick leaves. Compensation for private medical insurance. Co-working and gym/sports reimbursement. Budget for education. The opportunity to receive a reward for the most innovative idea that the company can patent. By applying for this position, you consent to the processing of your personal data as described in our Privacy Policy ( https://cloudlinux.com/candidate-privacy-notice ), which provides detailed information on how we maintain and handle your data.

- Оптимизация и масштабирование LLM-инференса (SGLang, vLLM, TensorRT-LLM). - Работа с моделями до 1T+ параметров и распределённой GPU-инфраструктурой.

Location: Москва Центр квантовых технологий Сбера проводит прикладные исследования в области оптических вычислений и развивает собственные ускорители вычислений. Команда создает программную инфраструктуру для работы с ускорителями: от интеграции с ML-фреймворками до прикладного ПО для удаленного использования оборудования. Мы ищем backend-разработчика, который поможет дорабатывать open-source решения, развивать собственные сервисы и автоматизировать эксперименты. Это возможность работать на стыке разработки, ML и аппаратных вычислений, участвовать в планировании и предлагать собственные технические идеи. Обязанности * Дорабатывать существующее ПО с открытым кодом для нужд лаборатории, например для использования нашего вычислителя в PyTorch * совместно с инженерами разрабатывать собственное ПО для работы ускорителей вычислений, включая CI/CD, автоматизацию экспериментов, проведение экспериментов и оформление результатов * создавать прикладное ПО для удаленного использования наших ускорителей вычислений * активно участвовать в планировании работы и предлагать новые идеи * проводить code review и вести собственные задачи в трекере * поддерживать качество кода через тестирование и развитие инженерных практик команды. Требования * Отличное знание Python: asyncio, multiprocessing, pandas, numpy, matplotlib/plotly * хорошее знание C++ или Rust * умение быстро разбить систему на минимальное количество компонентов * стремление к простоте реализации компонентов * опыт промышленной разработки в команде с git, CI/CD, написанием юнит- и интеграционных тестов и доставкой до прода * готовность работать с open-source решениями и разбираться в чужом коде * интерес к автоматизации экспериментов и работе с вычислительным оборудованием. **Будет плюсом:** * Знакомство с ML-стеком: PyTorch, ONNX * понимание устройства моделей CNN и Transformer * опыт работы с микроконтроллерами * опыт работы с FPGA * опыт интеграции вычислительного оборудования в ML-пайплайны * опыт создания сервисов для удаленного доступа к вычислительным ресурсам * навыки работы с генеративными AI-моделями * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. Условия * Комфортный офис на Кутузовском пр.32 * офисный формат работы * конкурентная компенсация (оклад и премии по результатам деятельности) * ДМС c первого рабочего дня, страхование от несчастных случаев и тяжелых заболеваний * материальная помощь и социальная поддержка, корпоративная пенсионная программа * дисконт-программы от компаний партнеров * бесплатный фитнес на территории работодателя * подписка Прайм с возможностью совместного использования на трёх близких * внешнее и внутреннее профессиональное обучение: семинары, тренинги, конференции, корпоративная библиотека.

Location: Москва Центр квантовых технологий Сбера проводит прикладные исследования в области оптических вычислений и развивает научно-технологические направления с потенциалом практического применения. Команда формирует и проверяет бизнес-гипотезы, превращая исследовательские идеи в работающие вычислительные решения. Мы ищем техлида, который возглавит разработку компиляторов для эффективного исполнения вычислений на современных ускорителях. Это возможность влиять на архитектуру ключевого технологического направления и работать на пересечении компиляторных технологий, ML и высокопроизводительных вычислений. Обязанности * Руководить командой разработки компиляторов * разрабатывать и развивать компоненты компилятора: промежуточное представление, оптимизации и codegen * использовать и расширять LLVM/MLIR, TVM, XLA или Triton для решения задач проекта * применять знания архитектур GPU/NPU и работать с CUDA или OpenCL * оптимизировать графы вычислений: выполнять fusion, tiling и memory reuse * интегрировать компиляторные решения с ML-стеком, включая PyTorch и ONNX * участвовать в формировании и проверке бизнес-гипотез центра. Требования * Опыт руководства командой разработки * опыт разработки компиляторов: работа с IR, оптимизациями и codegen * практический опыт хотя бы с одним из инструментов: LLVM/MLIR, TVM, XLA, Triton * понимание архитектур GPU/NPU * опыт работы с CUDA или OpenCL * практический опыт оптимизации графов вычислений: fusion, tiling, memory reuse * знакомство с ML **Будет плюсом:** * Опыт разработки компиляторов для специализированных ускорителей * опыт анализа и адаптации open-source компиляторных решений * знание внутреннего устройства PyTorch или ONNX Runtime * опыт профилирования и оптимизации производительности ML-моделей * интерес к оптическим и квантовым вычислениям * публикации или выступления в области компиляторов, ML-систем или высокопроизводительных вычислений * навыки работы с генеративными AI-моделями * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. Условия * Комфортный офис в Москве * офисный формат работы * конкурентная компенсация (оклад и премии по результатам деятельности) * ДМС c первого рабочего дня, страхование от несчастных случаев и тяжелых заболеваний * материальная помощь и социальная поддержка, корпоративная пенсионная программа * дисконт-программы от компаний партнеров * бесплатный фитнес на территории работодателя * подписка Прайм с возможностью совместного использования на трёх близких * внешнее и внутреннее профессиональное обучение: семинары, тренинги, конференции, корпоративная библиотека.

Location: Санкт-Петербург ИТ B2C — самая крупная экосистема в Сбере. Нас более 8000 человек в 18 городах России. Мы занимаемся разработкой и развитием розничных решений, помогая сделать сервисы Банка доступнее, безопаснее и удобнее. Ждем именно тебя! Обязанности * проектировать и разрабатывать LLM-workflow, AI-агентов и мультиагентные системы: настраивать взаимодействие моделей, работу с контекстом и вызов внешних инструментов * разрабатывать и улучшать RAG-решения: подготовку данных, векторизацию, поиск релевантной информации и формирование контекста для LLM * исследовать и применять подходы на основе Transformer и Attention для обработки текстов, извлечения сущностей и построения векторных представлений * интегрировать AI-решения со смежными банковскими продуктами и внешними сервисами * проводить эксперименты, оценивать и улучшать качество агентов и ML-моделей, настраивать мониторинг их работы * участвовать в построении масштабируемых MLOps-процессов для развёртывания и стабильной работы моделей в промышленной эксплуатации. Требования * опыт работы в машинном обучении от трёх лет, наличие реализованных и внедрённых решений * практический опыт разработки LLM-workflow или агентских систем, их интеграции и адаптации под бизнес-задачи * опыт работы с RAG и векторным поиском: разбиение данных на фрагменты, выбор embeddings, поиск и ранжирование результатов * знание основ машинного обучения и основных архитектур Deep Learning, в особенности Transformer и механизма Attention; понимание принципов работы и обучения LLM * уверенное владение Python, NumPy, Pandas, Scikit-learn и PyTorch, опыт написания кода для промышленной эксплуатации * опыт работы с LangGraph или аналогичными инструментами построения LLM-workflow и агентских систем * опыт работы с Big Data-стеком – Spark, Hadoop, Hive; умение строить и оптимизировать пайплайны обработки данных * базовые знания и практический опыт в MLOps: развёртывание, мониторинг и сопровождение моделей * умение оценивать качество решений, анализировать ошибки и делать обоснованные выводы по результатам экспериментов * самостоятельность, проактивность, ориентация на результат и готовность осваивать новые подходы. Будет плюсом: * опыт применения GigaChat, Kandinsky и аналогичных моделей в продуктах * опыт решения задач персонализации и разработки рекомендательных систем * опыт использования AI-инструментов для анализа, генерации контента и автоматизации рабочих процессов. Условия * комфортный современный офис * возможность выбрать удобный график – офис/гибрид * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Мы в Kandinsky строим платформу для генерации изображений и видео. Команда Harness отвечает за слой инструментов и пайплайнов вокруг моделей: он превращает исследовательские чекпоинты в решения, которыми пользуются миллионы пользователей. Мы ищем ML-инженера, который будет развивать этот слой, автоматизировать сценарии работы с визуальным контентом, влиять на выбор моделей и подходов и помогать командам доводить модели до продукта. Обязанности * разрабатывать агентные системы: сценарии, инструменты, оркестрация вызовов * решать прикладные задачи с помощью CV- и генеративных моделей: от постановки до работающего в проде решения * собирать пайплайны обработки и генерации изображений и видео, автоматизировать сценарии работы с визуальным контентом * адаптировать модели под конкретные домены и продуктовые кейсы * искать причины деградаций качества и предлагать способы их устранения * работать в связке с исследователями, продуктом и бэкендом, переводя продуктовые требования в технические решения. Требования * уверенное владение Python и фреймворками глубокого обучения (PyTorch) * понимание разницы между agentless- и agentic-решениями, опыт написания обоих и умение понимать, когда что уместно * понимание устройства актуальных CV- и GenCV-моделей * умение работать с контейнеризацией (Docker, Kubernetes) * опыт интеграции генеративных моделей * понимание бизнес-метрик и их переноса на модельные метрики * опыт работы с opensource-моделями: выбор под кейс, настройка гиперпараметров, дообучение Условия * комфортный современный офис рядом с м. Кутузовская * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * система обучения для профессионального и карьерного развития * расширенный полис ДМС с первого дня работы и страхование для семьи * льготная программа ипотеки для сотрудников * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

разрабатывать и дообучать модели TTS: проектирование, finetuning и адаптация генеративных подходов для высококачественного синтеза речи и zero shot клонирования голоса под задачи бизнеса; участвовать в дальнейшей разработке голосового пайплайна, объединяющего ASR, VAD, LLM, NLP и TTS для работы в режиме реального времени;

Allen Control Systems is looking for a Director of Computer Vision and Machine Learning to lead perception and tracking strategy, manage teams, and deliver production computer vision and machine learning systems.

Location: Singapore Workplace: Hybrid About Cantina: Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning. About the Role: Cantina is expanding, and we're looking for a Research Scientist to join our growing Singapore team! In this role, you will drive foundational research on video generation models, taking ownership across the full research cycle and driving post-training research. Furthermore, you'll collaborate closely with data, infrastructure, and adjacent modeling teams to translate research findings into durable model improvements. What You’ll Do: - Build and maintain scalable systems for ingesting, preprocessing, and delivering large-scale video data for model training - Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes - Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs - Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems - Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency - Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns - Build tooling to support deduplication workflows at scale, including near-dedup pipelines over large video corpora - Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance distillation and adversarial distillation, with a focus on preserving or improving generation quality while reducing inference cost - Develop reward models and preference-based fine-tuning pipelines that align video generation quality with human judgments across dimensions such as aesthetics, motion quality, and prompt adherence - Analyze the relationship between base model behavior and post-training outcomes, and work with the foundation model team to inform pretraining decisions accordingly What You’ll Bring: - Strong hands-on experience building or scaling large-scale data systems or pipelines for machine learning workflows - Experience with distributed data processing frameworks such as PySpark or Ray, and orchestration tools such as Airflow or equivalent - Familiarity with containerization and container orchestration, including Docker and Kubernetes - Experience working with cloud-based data storage and compute (AWS, GCS, and/or Azure), including tradeoffs around cost, throughput, storage layout, and access patterns - Familiarity with video and media processing tools such as FFmpeg, PyAV, DALI, or OpenCV - Familiarity with multimodal or media data, including video, image, text, and audio - Strong research background in post-training methods for large-scale diffusion or flow-based generative models, with deep hands-on experience in distillation across both inference efficiency and quality preservation - Experience with reward modeling or preference-based fine-tuning for generative models, including RLHF, DPO or equivalent alignment approaches - Solid understanding of the interplay between pretraining and post-training, and how base model properties affect distillation and fine-tuning outcomes - Proficiency in Python and modern machine learning frameworks, with a strong preference for PyTorch or JAX - Track record of independent research, with the ability to drive projects from initial idea through experimental validation - Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) preferred - Good understanding of the practical challenges involved in building reliable, scalable, and reproducible data workflows for machine learning systems Benefits We Offer: - Competitive salary and generous company equity - Personal time off and paid holidays - Health insurance - Global travel insurance: Covers you when traveling internationally - Monthly spending stipend: $500 (~S$635) - Equipment: All equipment needed for your home office

Location: Москва Приглашаем исследователей в области машинного обучения в команду Рекомендательных систем и персонализации Центра практического ИИ. Центр практического искусственного интеллекта - стратегическое подразделение Сбера, создающее наукоемкие технологии. Мы создаем новые алгоритмы рекомендательных систем и адаптируем SOTA-подходы для задач банка и компаний-партнеров, помогаем бизнесу внедрять новые технологии. Также мы разрабатываем собственные open-source фреймворки и публикуем результаты наших исследований на международных конференциях уровня A/A\*. Приглашаем присоединиться к исследованиям и прикладным проектам в области рекомендательных технологий, DL-моделей для последовательных данных и подходов на стыке LLM и RecSys. Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика ждите сообщение от него в сберчате, диалог займёт примерно 10 минут. Задача AI-рекрутера — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. AI-рекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным для всех! Обязанности ### Наши задачи: * развиваем библиотеку для рекомендательных систем RePlay [https://github.com/sb-ai-lab/RePlay](https://github.com/sb-ai-lab/RePlay) * разрабатываем и улучшаем алгоритмы рекомендательных систем * разрабатываем и обучаем трансформерные модели на последовательностях событий на последовательностях событий на банковских данных * проводим исследования в области sequential recommendations, LLM для рекомендаций, foundation models для событийных данных sequential recommendations и генеративных рекомендаций * разрабатываем и внедряем AI агенты для банка * пишем статьи и выступаем на ведущих мировых и российских конференциях. Требования ### Мы ожидаем: * опыт работы от 2 лет в области, связанной с RecSys / DL / NLP * умение писать читаемый и хорошо структурированный код на python * знание стандартных библиотек и фреймворков (numpy, pandas, polars, scikit-learn, pytorch, pytorch-lightning, transformers) * навыки организации и воспроизводимого проведения ML-экспериментов (Hydra, Сlearml, Mlflow, W&B, Neptune) * опыт разработки и обучения моделей с помощью pytorch * знание математики, глубокое понимание классического ML и современных методов DL, широкий кругозор в ML в целом * понимание современных Transformer-based архитектур для работы с последовательностями * знание ключевых рекомендательных подходов и методов их оценки * умение читать, анализировать и воспроизводить научные статьи. Будет плюсом: * опыт успешного участия в ML соревнованиях и хакатонах * опыт написания научных статей, наличие публикаций * понимание принципов работы современных LLM и LLM-агентов. Условия ### Мы предлагаем: * комфортный современный офис г. Москва, рядом с метро Кутузовская * гибридный формат работы * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Lambda is looking for a Staff Product Manager to define and deliver observability products, telemetry capabilities, and diagnostics for cloud customers and operators.

Location: Serbia; Cyprus; London, United Kingdom; Amsterdam, Netherlands; Singapore; Georgia We are looking for a Head of ML / Head of Quant Research to take full ownership of developing and scaling machine learning trading strategies in crypto markets (Spot / Perpetuals). You will take over a live, operating production ML trading system with existing Capital Under Management and lead a dedicated research team. Key Responsibilities Full Ownership: Take complete control of the research pipeline, model architecture, signal generation, and risk limits. Alpha Generation: Design, backtest, and deploy new profitable ML models and quantitative trading strategies. Execution & Scaling: Optimize execution algorithms, refine live strategy performance, and scale capital under management. Leadership: Lead, mentor, and grow a team of quant researchers and developers. Requirements Education & Mathematical Background: Degree in CS / Math / Data Science from a top-tier university (MIPT, HSE, MSU, SPbSU, ITMO, YSDA, or international equivalents). Strong foundation in probability, statistics, linear algebra, and algorithms. Tech Stack & Hard Skills: Python (NumPy, Numba). PyTorch or JAX (for the ML track) OR advanced backtesting/quantitative modeling frameworks (for the Quant track). Proven track record of developing profitable ML models or quantitative trading strategies in production. Nice to Have: Experience in crypto/HFT/algorithmic trading (Order Book dynamics, L2/L3 data, funding rate arbitrage, liquidations). Proficiency in C++ (reading, profiling, and optimizing performance). Competitive programming / Kaggle achievements (IMO, IOI, ICPC, Codeforces, Kaggle Master/GM) or top-tier publications (NeurIPS, ICML, ICLR). English: A2+ (Internal team communication is in Russian; fluent spoken English is not required). Benefits Competitive fixed salary. PnL share (individual agreement). Direct influence on risk limits, capital allocation, and research roadmap. Remote work or office in Serbia, official contracting via Dubai / Serbia.

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Reddit is a complex consumer ecosystem where users, content, communities, and conversations interact over time. We are looking for a very senior data science thought leader to shape how Reddit understands and improves that ecosystem for the user / consumer flywheel. This role will define the measurement, modeling, and experimentation strategy for major Consumer questions: how users discover value, form habits, and deepen engagement; how we understand and serve user intent; how content and communities evolve; and how our systems create durable value for users, contributors, communities, and Reddit. The ideal candidate will bring deep expertise in one or more areas of consumer science, such as: Recommender systems and information retrieval User modeling, personalization, lifecycle, LTV, or optimization Content, community, or ecosystem modeling Knowledge graphs and representations of users, content, and communities Causal inference, experimentation, and long-term consumer measurement You will partner with leaders across Product, Engineering, Research, and Community to turn ambiguous consumer problems into clear scientific questions, durable analytical systems, and decisions that improve Reddit at scale. What you’ll do Set the scientific direction for high-impact Consumer problems spanning discovery, relevance, personalization, user growth, retention, content, and communities. Build a durable understanding of users, their intent, their needs, and the behaviors that lead to lasting value Develop models and frameworks that represent users, content, communities, and their relationships over time for use in systems that operate at scale Determine how to measure consumer and ecosystem health when objectives compete and short-term engagement may differ from long-term value. Set learning agendas and steer experimentation strategy to distinguish durable improvement from temporary metric movement. Build state-of-the-art measurement systems to evaluate heterogeneous effects across user personas, acquisition cohorts, platforms, content types, and community stages, and guide their actionability in production environments Ensure coherence around ecosystem interactions including feedback loops, selection effects, interference, distribution shift, content supply, and other ecosystem risks. Translate complex technical findings into clear product and business decisions. Develop exceptional scientists through mentoring and influence data science org health and direction Requirements Deep expertise in at least one relevant area, with the breadth and curiosity to work across adjacent domains. A deep understanding of how to model and optimize systems involving users, content, communities, and feedback loops. Ability to frame and lead the most ambiguous questions, identify the most important uncertainties, and develop a rigorous path from evidence to action. Exceptional technical judgment across model design, objective selection, validation, experimentation, and production tradeoffs. Advanced knowledge of modern machine-learning methods for behavioral and content data, including representation learning and sequential or contextual models. Experience with deep-learning frameworks such as PyTorch or JAX. Experience working across Product, Engineering, Research, and other partner teams. Excellent written and verbal communication, with the ability to influence senior stakeholders and explain technical ideas clearly. Significant experience applying data science, machine learning, statistics, experimentation, or causal inference to complex consumer products. Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $313,000 — $365,100 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: Amsterdam, Netherlands; Berlin, Germany; London, United Kingdom; Poland; Prague, Czech Republic; Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Role Summary Nebius Token Factory is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable, cost-efficient endpoint. The work sits at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment. What You Will Do Own a platform-level optimization pillar such as quantization, speculative decoding, KV -cache optimization, engine selection, or long-context serving. Create reusable evaluation, benchmarking, and rollout frameworks across multiple model families. Provide technical direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers on shared performance goals. Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems. Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV -cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. Build reproducible benchmark harnesses for TTFT , TPOT , tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations. Required Qualifications Strong Python and PyTorch engineering skills. Hands-on experience deploying or optimizing LLM, VLM , or high-throughput transformer inference systems. Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems. Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs. Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams. Preferred Qualifications Experience with quantization-aware training, post-training quantization, FP8 , INT8 , INT4 , NVFP4 , MXFP4 , AWQ , GPTQ , SmoothQuant, or related techniques. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods. Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role. Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.