Данные и ML вакансии

Location: Remote USA; Canada Workplace: remote ABOUT YOU We are looking for an accomplished Principal Machine Learning Engineer to join our global ML organization. In this role, you will drive innovation across our machine learning ecosystem, architect advanced ML solutions, and mentor junior ML engineers around the world. You will play a key part in shaping our technical direction—leading complex ML initiatives, elevating engineering standards, and guiding teams as they build scalable, production‑ready machine learning systems. If you are ambitious, energized by solving challenging technical problems, passionate about developing talent, and excited to influence the future of ML/AI in the video game industry, this could be the perfect role for you. ABOUT US   Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. Requirements: Modeling Depth Advanced Degree in Statistics, machine learning or related areas. Experience in statistics/ML expertise with a track record of leading high-impact data science initiatives at scale of billions of transactions. Hands-on experience creating, training and fine-tuning models not just integrating hosted model APIs. You should be able to walk through  the data, the objective, what broke, and the before/after evaluation numbers, and why the model did not perform as expected. Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks. Production experience with classical ML for fraud/anomaly detection, recommendation, or churn/LTV (gradient boosting, deep learning, graph-based models). Technology Familiarity Supervised learning, transfer leaning on machine learning as well as neural networks Basic LLM knowledge, especially how to use it and where not to use it. MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines. Model serving and inference optimization (vLLM-class serving, quantization). Nice-to-Have: Publications, conference talks, or recognized open-source contributions to training/eval tooling . Graph-based fraud detection (fraud rings, device/account linkage).. Gaming, payments,  fraud, advertising  domain experience. Hands-on, up-to-date experience with modern AI tools (e.g., Claude, Copilot, Cursor) for code generation, review, and accelerating day-to-day engineering work. Benefits: We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each US employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play.   Equal Employment Opportunity Statement: Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law. We consider qualified applicants with criminal histories in accordance with the Fair Chance Act.   Criminal History Consideration: For the Data Scientist position, we will conduct a background check that may include the following: Criminal history check Employment verification Education verification   Relevance to Job Responsibilities: The background check is relevant to this position because of the following role responsibilities: Accessing confidential company data Ensuring compliance with regulatory requirements   Rights Under the Fair Chance Act: Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com.

Задачи: — Проектировать микросервисы и API (REST/gRPC); — Создавать RAG и AI-агенты: разрабатывать системы генерации ответов на основе документов, цепочек рассуждений и логики Tool Calling (LangChain/LangGraph/LlamaIndex); — Работать с данными и текстом: парсинг, очистка и чанкинг текстов, извлечение сущностей (NER), классификация, реализация гибридного семантического и полнотекстового поиска; — Проектировать базы данных: работать с реляционными (PostgreSQL), векторными (Qdrant/Milvus/pgvector) и графовыми (Neo4j) базами под задачи GraphRAG; — Заниматься оптимизацией производительности, покрытием кода тестами (Pytest), написанием асинхронного кода на Python (FastAPI/Django); — Проводить code review и контейнеризацию приложений (Docker). Требования: — Опыт коммерческой разработки на Python от 2 лет для выпускников профильных факультетов ведущих вузов (МФТИ, НИУ ВШЭ, МГУ, ИТМО, МГТУ им.— Уверенное знание Python, асинхронного программирования, веб-фреймворков (FastAPI) и принципов проектирования микросервисной архитектуры (REST, Swagger, gRPC); — Практический опыт работы с LangChain, LlamaIndex, LangGraph, современными LLM (GPT, open-source модели) и фреймворками для создания AI-агентов; — Опыт обработки и классификации документов различных форматов (PDF, сканы), извлечения сущностей (NER, гибридный поиск) с использованием Transformers, BERT, SpaCy, Natasha; — Опыт работы с реляционными БД (PostgreSQL), NoSQL/кэшированием (Redis), векторными БД (Qdrant/Milvus/pgvector) и графовыми системами (Neo4j); — Опыт работы с брокерами сообщений (RabbitMQ, Kafka); — Владение инструментами контейнеризации и CI/CD (Docker, Git, S3); — Понимание полного жизненного цикла разработки ПО (SDLC); — Опыт работы по ТЗ с чтением схем (UML, BPMN).

Задачи: - Участвовать в разработке моделей для продуктивной среды; - Строить модели машинного обучения, улучшать качество; - Писать код, проводить code-review; - Предлагать способы оптимизации и совершенствования существующих инструментов. Требования: - Знание Python; - Опыт работы в рекомендательных системах/поиске; - Опыт работы в роли mle/ds от 5ти лет; - Знание теоретических и практических продвинутых методов машинного обучения; - Знание SQL, знакомство с hadoop-стэком; - Опыт работы с docker, kubernetes, pyspark, kafka, postgres, airflow; - Наличие опыта вывода разработанных решений в прод.

Location: New York; San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. WHAT YOU’LL WORK ON: - Improve existing methods and develop new techniques for large-scale model training, optimization, and inference - Extend models to long-context and long-horizon tasks - Improve inference-time efficiency, reliability, and robustness in high-stakes real-world deployments YOU MAY BE A FIT IF: - You’re currently pursuing a PhD in computer science, machine learning, or a related field - You have a demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas - You have experience developing and evaluating large-scale models or machine learning systems - You’re familiar with distributed training, large-scale inference, or multi-GPU environments - You have publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR - You have strong programming and engineering skills and can translate research ideas into working implementations - You have a collaborative, mission-driven mindset and can work effectively across research and engineering teams

Задачи: — разработка систем автоматизации казначейства, риск-менеджмента, отчётности — добавление AI для точных прогнозов Требования: — опыт работы с Python от 5 лет — экспертный уровень владения SQL — знание PySpark — понимание принципов SOLID и asyncio — опыт работы с HDFS и Kafka Вакансия открыта в трёх продуктовых командах с одинаковым базовым стеком и уровнем задач: — BigData: Spark (batch+streaming), сложные SQL-оптимизации, DWH/Data Lake — Микросервисы: Python (FastAPI/Django), API, интеграции с системами

Location: Athens; Limassol; Nicosia; Thessaloniki Workplace: hybrid The Role:  We are looking for a Senior Data Scientist specializing in Graph AI to join our growing Data Science team. In this role, you will design and deploy production-grade graph machine learning solutions, working with Graph Neural Networks (GNNs), graph databases, and large-scale datasets to solve complex real-world problems.   You will play a key role in shaping the company's Graph AI capabilities, collaborating with cross-functional teams and mentoring other data scientists while delivering innovative, business-impacting solutions. The main responsibilities of the position include: Design, develop, and deploy Graph Neural Networks (GNNs) and graph-based machine learning solutions Transform large, complex datasets into graph representations and extract actionable insights using advanced analytics, graph algorithms, and statistical techniques. Drive the adoption of Graph AI across the organisation by identifying new use cases and contributing to the evolution of the company's data science strategy. Communicate complex technical concepts and model outcomes clearly to both technical and business audiences. Ensure high standards in data preparation, feature engineering, model evaluation, documentation, and code quality . Leverage technologies such as Python, Graph Neural Networks, graph databases, modern ML frameworks, and cloud-based data platforms to build innovative data solutions Ensure adherence to best practices in data quality, model monitoring, version control, and reproducible analytics Lead and supervise a team of data scientists working on graph-related tasks Partner closely with Data Engineers, Software Engineers, Product Managers, and business stakeholders to translate business challenges into scalable Graph AI solutions. Main requirements: University degree in Mathematics, Physics, Computer Science, Engineering, Data Science, or a related quantitative field At least 6 years of experience in data science, machine learning and AI , including hands-on experience in designing, training and optimising Graph Neural Networks (GNNs) for production applications. Experience working with relational and non-relational databases Solid experience working with graph databases (e.g., Neo4j, Neptune) and proficiency in graph query languages (e.g., Cypher or Gremlin). Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly Strong programming skills in Python and experience with Pandas, NumPy , and similar data analysis libraries Strong analytical thinking, problem-solving ability, and attention to detail . Excellent communication skills and the ability to work collaboratively in a team environment Fluent in English Benefit from: Attractive remuneration package plus performance related reward Private health insurance Corporate pension fund Intellectually stimulating work environment Continuous personal development and international training opportunities The Hiring Experience: What Awaits You Let’s Connect – Intro Chat with Talent Acquisition Deep Dive – First Interview with Your Future Team Bring It to Life – Role-Specific Take-Home Task Final Connection – Final Interview All applications will be treated with strict confidentiality!   By submitting this application, I confirm that I have read and agree to the terms of the Privacy Policy and the Confidentiality Undertaking.

Задачи: - Разработка co-pilot решения на базе данных RAG и применении LLM - Разработка и интеграция AI-агентов, исследование новых поверхностей взаимодействия с AI - Разработка микросервисов на Python в качестве MCP инструментов - Анализ требований, формализация функционала, проектирование архитектуры взаимодействия агентов и сервисов - Участие в планировании, декомпозиции задач и оценке сложностей, планировании спринтов на основе SCRUM церемоний - Проведение код-ревью, поддержание высокого стандарта качества и чистоты кода - Тесное взаимодействие с командой на всех этапах разработки - Участие в подготовке и выпуске релизов, взаимодействие со смежными подразделениями - Документирование своей работы и менторство джуниор-разработчиков Требования: - Опыт коммерческой разработки на Python от 4 лет - Уверенное знание Python 3.7+ (в проекте используется 3.12+) - Понимание и успешный опыт применения паттернов проектирования (GoF, SOLID, GRASP) - Опыт проведения код-ревью и работы по GitFlow - Опыт разработки агентных систем с помощью langchain, langgraph и успешное внедрение в промышленную среду - Опыт разработки приложений, основанных на микросервисной архитектуре - Уверенное знание SQL и понимание принципов работы реляционных СУБД (PostgreSQL) - Опыт написания unit-тестов - Опыт разработки асинхронных приложений на Python - Понимание жизненного цикла разработки приложений в контексте Agile - Опыт и желание работать в команде, умение аргументировать свои решения и договариваться Условия: - Возможность участия в интересном проекте - Возможность профессионального и карьерного роста в компании - Опыт работы в команде профессионалов - Уровень заработной платы обсуждается индивидуально - Удаленный формат работы и офисный формат в Москве

Задачи: - Поддержка и развитие существующих AI-модулей системы онлайн-прокторинга - Определение отсутствия лица и нескольких лиц в кадре - Реализация Face ID и проверки личности пользователя - Разработка Liveness-проверки (моргание, улыбка, движения головы, контроль дистанции до камеры) - Определение поворотов головы и направления взгляда - Анализ положения тела и правильности посадки пользователя - Выявление ухода пользователя из кадра и поворота от рабочей зоны - Обнаружение посторонних людей и объектов в кадре - Детекция смартфонов, наушников, часов и других гаджетов - Распознавание мониторов, ноутбуков, клавиатур, мышей и других объектов рабочего места - Определение посторонних движений и плохого освещения - Снижение количества ложных срабатываний моделей - Обучение, дообучение и замена AI-моделей - Оптимизация моделей с использованием CUDA - Интеграция AI-модулей через API - Развертывание локальных LLM на собственных серверах - Работа с моделями Qwen, Llama, Mistral, Gemma и другими open-source LLM - Интеграция GPT, Claude, Gemini и других LLM через API - Подбор и оптимизация AI-моделей под задачи продукта - Внедрение новых AI-библиотек и технологий Требования: - Уверенное владение Python - Практический опыт работы с Computer Vision - Практический опыт работы с LLM - Опыт локального развертывания open-source LLM - Опыт интеграции облачных LLM через API - Умение работать с существующим кодом и готовыми моделями - Опыт запуска и оптимизации моделей на GPU - Готовность поддерживать существующие AI-модули и разрабатывать новые - Самостоятельность и умение доводить решения до production - Проживание в Астане Будет плюсом: - Опыт работы с Hugging Face и Ollama/vLLM - Опыт использования TensorRT, ONNX и OpenVINO - Опыт разработки систем реального времени - Опыт создания AI-продуктов для Computer Vision Условия: - Полная занятость - Возможна стажировка - Официальное трудоустройство - Формат работы: удаленно - График 5/2 или свободный - Рабочий день — 8 часов или по договоренности - Заработная плата от 400 000 до 600 000 ₸ до вычета налогов - Долгосрочная работа над действующим AI-продуктом - Формат работы и уровень оплаты обсуждаются индивидуально

Задачи: — Анализировать опоздания и "плохо построенные" маршруты. — Писать промышленный код и выводить его в продакшн. — Общаться с заказчиком, формализовывать требования. — Креативить, предлагать новые идеи. Требования: — Уверенное знание Python и стандартных DS библиотек. — Знание хотя бы одного диалекта SQL (предпочтительней PostgreSQL). — Знание основ ML и задач дискретной оптимизации. — Высшее образование в одной из сфер: математика, компьютерные науки.

Location: Canada; Calgary; Winnipeg; Vancouver; Halifax; Edmonton; Toronto; Victoria; Montreal Workplace: Remote WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? We are on a mission to build machines that understand the world and make them safely accessible to all. Data quality is foundational to this process. Machines (or Large Language Models, to be exact) learn in similar ways to humans, by way of feedback. By labelling, ranking, auditing, and correcting model output, you will improve Large Language Models' performance for iterations to come, thus having a lasting impact on Cohere's technology. We are hiring Generalist professionals with broad backgrounds that span multiple consumer-facing or personal domains. This is a judgment-driven role, not passive data entry. You will review, assess, and provide structured feedback across a broad and evolving range of tasks, evaluating, stress-testing, and improving our models on English-language data spanning multiple modalities (text, image, and structured formats such as JSON, CSV/TSV, and Markdown). This is a great opportunity for professionals with strong analytical skills to contribute to high-impact annotation projects. Please Note: This is a part-time independent contractor position available within Canada only. We seek candidates who can commit to 16 hours per week at a CAD $30/hour contract rate. This role is BYOD 💻 (Bring Your Own Device, laptop). This position is remote. As an Data Annotation Specialist, you will: - Evaluate and rank model outputs: Complete preference and comparison tasks, assessing which responses best conform to project guidelines for accuracy, helpfulness, tone, and safety, and writing clear justifications for your judgments. - Stress-test and break models: Probe models adversarially to surface failure modes, unsafe behavior, and capability gaps, and document reproducible cases that engineering and research teams can act on. - Create datasets: Author high-quality prompts, responses, and exemplars to build training and evaluation datasets, following detailed specifications and editing machine-written or human-written outputs to standard. - Build and apply rubrics and taxonomies: Contribute to the design of grading criteria and rubrics, then apply them consistently to produce structured, high-quality annotations across task types. - Annotate and correct multimodal data: Label, audit, and rectify inaccuracies across text, image, and structured data, maintaining a high standard of data integrity and accuracy. - Calibrate and maintain consistency: Participate in calibration exercises and inter-annotator agreement checks to align on standards, and flag ambiguous or uncovered edge cases rather than guessing, since a single misjudgment replicated at scale degrades a model. - Adapt to experimental work: Take on new and evolving task types as project needs shift, applying sound judgment in areas where guidelines are still being developed. - Report on model performance: Surface and communicate quality and performance trends in model and agent behavior, giving cross-functional partners clear, well-evidenced feedback on where models succeed, fail, and degrade. You may be a good fit if you have: - 1+ years of experience in AI data annotation, LLM evaluation, content moderation, research, or a related analytical role, with exposure to quality assurance, and/or preference ranking. - Experience applying detailed guidelines to complex and often ambiguous content, with strong contextual and sociocultural judgment, sensitivity to nuance, tone, and register, and the ability to reason well in cases where there is no single correct answer. - Comfort with ambiguity: a willingness to flag unclear or uncovered edge cases rather than guess, and to work productively on novel, experimental tasks whose definitions are still evolving. - A sharp, curious eye for inconsistencies, subtle errors, and model failure modes, including the instinct to probe models adversarially and surface where they break. Familiarity with how large language models behave, such as hallucination, sycophancy, and instruction-following gaps, is a plus. - Excellent command of written English and strong reading comprehension, with the ability to clearly justify your evaluations, including why an output is correct or incorrect, high-quality or low-quality, and to write clear prompts and exemplars that explain your reasoning. Bonus points if you are fluent in another language! - Strong attention to detail and commitment to accuracy, with the ability to maintain consistency across high-volume and monotonous tasks. - Comfort working with annotation platforms and structured formats such as JSON, CSV/TSV, Markdown, XML, and YAML. - Strong execution in a remote environment, including good time management, comfort using new tools, and the ability to work independently in a global, asynchronous team. Candidate Journey - Initial Screening: Our Talent Team will review your application. - First Assessment: If selected to move forward, you’ll receive a multiple-choice assessment by email. - Second Assessment: Candidates who achieve a passing score will be contacted by a member of our team to complete a more comprehensive assessment focused on their area of expertise. - Video Screen: If selected to move forward, you’ll have a short video call with a member of our Operations Team. - Final: Selected candidates will receive an Independent Contractor Agreement. As an independent contractor, you maintain control over how you complete your work and may work with multiple clients simultaneously, although we ask you to declare if any of these are with a direct competitor of Cohere and maintain IP confidentiality of the Cohere project. Independent contractors are not eligible for health benefits or other benefits provided to employees. Compensation for services is provided to contractors by contractors invoicing for services provided pursuant to the terms of our agreement with the contractor. It is important to understand that as an independent contractor, continuous work is not guaranteed. The client-contractor relationship is fundamentally project-based, meaning engagements may be temporary, periodic, or intermittent based on our organizational needs and project availability. As an independent contractor, you should anticipate fluctuations in workflow and, therefore, compensation for services when Cohere does not require as many hours of services in a week. Prospective candidates, please be advised: this role involves working with human-generated and model-generated tasks that may involve exposure to not safe for work (NSFW) text content as part of data annotation tasks, including explicit, offensive, or other inappropriate material.

Задачи: — разработка AI-решений для цифрового банкинга и персонализированных финансовых сервисов на облачной платформе — архитектура ML/AI и доставка моделей промышленного уровня — управление жизненным циклом моделей и практики MLOps — проектирование масштабируемых AI-платформ с поддержкой множества кейсов — обеспечение ответственного использования AI, управление моделями и рисками — взаимодействие с заинтересованными сторонами и стратегическое согласование Требования: — опыт в Data Science / AI не менее 5 лет, включая руководящий опыт — опыт в банковской сфере/финтехе (кредитный скоринг, борьба с мошенничеством, персонализация) — навыки управления жизненным циклом моделей и MLOps — уровень английского от Upper-Intermediate и выше Условия: — работа в проектах, связанных с цифровым банкингом и финансовыми сервисами — график работы: воскресенье–четверг (GMT+3) — локации: Грузия, Казахстан, Сербия, Молдова, Азербайджан, Беларусь

Location: Paris Workplace: OnSite ABOUT VOODOO Voodoo is a consumer tech company founded in Paris in 2013, with one mission: entertain the world. Today we are 1,000 people, generating $1b+ in annual revenue, profitably. Our games and apps have passed 7 billion downloads and reach 200 million+ active users every month, making Voodoo the #3 mobile publisher worldwide in downloads, after Google and Meta. We were backed by Goldman Sachs, Tencent and GBL along the way. TEAM Blitz is a gaming platform launched by Voodoo in 2021, allowing players to compete on mobile games for real cash prizes (head-up, tournaments, leagues, etc.). It is leveraging Voodoo's unique game portfolio to engage users in a fun and rewarding way. ROLE This role sits at the intersection of the Data and Gaming teams. You'll be the analytical partner to game designers and product managers, owning the measurement and tuning of the games themselves — from difficulty and game parameters to matchmaking fairness and the in-game economy. Your work directly shapes what players experience and how the games perform. MISSIONS: - Own the A/B testing cycle for games and game parameters — designing experiments, validating changes, and reading out results that drive core KPIs (engagement, RTP, rake, fairness, retention). - Analyze and calibrate game parameters — difficulty, scoring, completion rates, seed selection, and payout structures — to hit target player experience and economic goals. - Translate analyses into clear, actionable recommendations that shape game design and the product roadmap. - Build simulations and prototypes to pressure-test parameter and economy changes before rollout, then monitor them live once shipped. - Collaborate closely with game designers and engineers to embed data-driven decisions across the full game lifecycle. - Build and maintain dashboards and reports that keep the Gaming and Data teams aligned on critical game and economy metrics. PROFILE - 3+ years in analytical roles, ideally within gaming or a data-intensive product environment, comfortable working directly with product and design teams. - Skilled in SQL and Python; experience with large, complex datasets. - Strong grasp of A/B testing and statistical methods. - Comfortable with simulation and modeling to reason about game parameters, difficulty, and game economy outcomes. - Thrives in a fast-paced environment with rapid iteration. - Player-first mindset with genuine curiosity about games, player behavior, and game balance. BENEFITS - Competitive salary based on experience - Swile Lunch voucher - Gymlib (100% covered by Voodoo) - Premium healthcare coverage with SideCare, 100% covered for you and your family - Wellness activities in our Paris office - Remote days on Fridays

Задачи: — Разработка и внедрение AI агентов и моделей машинного обучения — Создание и экспериментирование с GenAI моделями и агентными рабочими процессами Требования: — Владение Python и другими релевантными языками программирования — 3 года практического опыта с фреймворками и библиотеками машинного обучения (TensorFlow, PyTorch или аналогичные) — 2 года практического опыта в разработке и развертывании AI агентов и моделей машинного обучения — Глубокие знания облачных технологий, с акцентом на Microsoft Azure, включая платформы Azure Data и AI (Databricks, Fabric, AI Foundry)

Обязанности: - Разрабатывать и обучать модели машинного обучения, включая генераторы кандидатов - Улучшать модели оценки стоимости автомобилей - Разрабатывать решения для антифрода и модерации контента - Подготавливать и трансформировать данные для обучения и продакшена моделей - Упаковывать модели в сервисы и внедрять их в продакшен Требования: - Опыт оркестрации ML‑пайплайнов с помощью Airflow или Luigi - Умение программировать на Python или C++ - Навыки работы с SQL и NoSQL для подготовки и трансформации данных - Умение разворачивать решения в виде микросервисов с использованием FastAPI, gRPC или Docker - Хорошие теоретические знания в области машинного обучения - Понимание устройства и принципов работы одного из ML‑фреймворков или библиотек, например CatBoost или PyTorch

Location: San Francisco Bay Area, CA Workplace: onsite Overview Get Paid to Help Shape the Future of Technology  We are building a community of participants for upcoming in-person data collection projects in San Francisco Bay Area. These projects help improve the next generation of smart devices and technology and we are looking for everyday people to take part.  No experience is required. No special skills are needed. Simply attend your scheduled session, follow a few simple instructions, and get paid for your time.  What You'll Do  Participants take part in short, moderated sessions involving simple, everyday activities such as walking, sitting, or moving through a space while smart devices record the session. A friendly moderator guides you through every step. A trained moderator will guide you through the entire process.  Project Details Job Type: Freelance/Independent Contract Time Commitment:Approximately 1 hour Location:San Francisco Bay Area (On-site) Project Type: Short-term, paid, in-person research sessions   Who Can Apply?  We welcome people of all ages and backgrounds from across the San Francisco Bay Area. To participate, you should:  Adults and minors are eligible (participants under 18 require parent/guardian consent)  Be comfortable following simple instructions in English (non-native English speakers are welcome).  Feel free to register with friends or family, you are welcome to participate together. Why Join Welo Data?   ✨ Limitless Flexibility   Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.    🌱 Limitless Growth   Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.    🌍 Limitless Support   Be part of a global contributor community with responsive guidance and support.    💡 Real Impact   Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.       About Welo Data  Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries.   At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.

Location: Limassol; Athens; Nicosia Workplace: hybrid The Role: Are you passionate about enterprise database systems, adept with both on-premises and cloud environments, and driven by operational reliability?   If you thrive in a dynamic environment where performance tuning, data security, and high availability fuel your growth, we want to connect with you! Join our team as a Database Administrator and be part of a community that values your expertise and encourages your continuous advancement. The main responsibilities of the position include: Manage and support various databases (Oracle, MySQL, PostgreSQL, MongoDB) on-premises as well as in the cloud, handling daily support, performance optimisations, infrastructure improvements, backups, and replication streams Be the primary DBA for some databases, collaborate with other DBA team members to keep the tasks evenly distributed between everyone Handle migrations of various data sets and schemas between distinct database environments Monitor database performance metrics continuously and proactively troubleshoots and resolves structural or transactional issues Provide technical direction and guidance towards DB configuration Work in close cooperation with the Information Security Department to maintain security, access rights and compliance of all databases Recommend and implements emerging database technologies Collaborate effectively with other departments to achieve operational and project related goals Main requirements: BSc/MSc in Information Technology or any other relevant field Minimum 3 years’ experience in a similar position, preferably in the financial services industry Hands-on experience with major DB engines (MSSQL, MySQL, Oracle, PostgreSQL, MongoDB, or ClickHouse) alongside strong SQL writing and optimization skills Solid knowledge of AWS services (RDS, DMS, S3, IAM), with additional cloud experience (Azure, Oracle Cloud, MongoDB Atlas) considered a plus Strong knowledge of Linux/Windows environments, Shell scripting, and database/infrastructure monitoring tools (CloudWatch, OEM, Zabbix, Percona Toolkit, NewRelic) Excellent verbal and written communication skills in English Proven ability to collaborate effectively across cross-functional teams High accountability, strong attention to detail, and an adaptable mindset focused on continuous upskilling and operational efficiency Benefit from: Attractive remuneration package plus performance related reward Private health insurance Corporate pension fund Intellectually stimulating work environment Continuous personal development and international training opportunities The Hiring Experience: What Awaits You Let’s Connect – Intro Chat with Talent Acquisition Deep Dive – First Interview with Your Future Team Test your edge – Aptitude / Personality Assessment Final Connection – Final Interview All applications will be treated with strict confidentiality!   By submitting this application, I confirm that I have read and agree to the terms of the Privacy Policy and the Confidentiality Undertaking.

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