Вакансии

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.

Location: Москва Мы команда в Департаменте корпоративной архитектуры, создаем и внедряем GenAI-решения. Отвечаем за повышение эффективности ИТ-решений и автономизацию принятия архитектурных решений. Создаем решения, которые дают значительный бизнес-эффект. Обязанности ### Обязанности * руководство командой разработки (2 человека) * проектирование, разработка и внедрение мультиагентных систем: от генерации идеи и сборки MVP до успешного запуска в продакшн * выстраивание процесса оценки и тестирования качества работы агентов (evals) * перевод бизнес-потребности на язык машинного обучения и предлагать оптимальные архитектурные решения * оборачивание разработанной модели и сложную бизнес-логику в надежные микросервисы на Python * дообучение LLM (на базе GigaChat) под специфичные бизнес-задачи и предметные области (Fine-tuning, LoRA) Требования ### Требования * от 5 лет опыта в коммерческой разработке (Data Science / NLP / Backend) с уверенным практическим применением LLM * опыт создания и успешного вывода в продакшн enterprise-решений на базе AI-агентов * от 1 года опыта технического лидерства или менторства * LLM & Agents: глубокий опыт разработки LLM-пайплайнов, RAG-систем и мультиагентных архитектур. Знание и применение фреймворков: LangChain, LangGraph, DeepAgents (или аналогов) * NLP & ML: фундаментальное понимание NLP (от классики TF-IDF до современных трансформеров) и алгоритмов классического ML * Python Backend: понимание принципов проектирования микросервисной архитектуры (REST API); опыт написания асинхронных и многопоточных приложений (aiohttp, asyncio, multiprocessing) * опыт интеграции ML-моделей и AI-агентов в высоконагруженные системы * инструменты: свободное владение Git, SQL, Bash, Docker * готовность проводить код-ревью и давать обратную связь. ### Будет плюсом * интерес к SOTA-подходам в агентной разработке и LLM * опыт работы с Kubernetes и понимание принципов CI/CD * участие в соревнованиях Kaggle или аналогичных. Условия ### **Условия** * гибридный формат работы из г. Москва * годовой бонус и ежегодный пересмотр * расширенный ДМС с первого дня + стоматологию и льготное страхование для семьи * корпоративный университет Сбера, внутреннюю образовательную платформу, участие в IT-конференциях * офис на Кутузовской с зонами отдыха и спортзалом * льготную ипотеку в Сбере, корпоративную пенсионную программу, подписку СберПрайм с возможностью совместного использования на трёх близких, скидки от партнеров и сервисов группы компаний.

– Обучение в российском вузе на 3-6 курсе бакалавриата и специалитета или 1-2 курсе магистратуры. – Наличие любого опыта работы, релевантного направлению стажировки, а также опыта в проектах.

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 continuing to grow our teams with the best talent. This role is completely remote friendly within the United States. If you happen to live close to one of our physical office locations (San Francisco, Los Angeles, New York City & Chicago) our doors are open for you to come into the office as often as you'd like. Safety is central to Reddit’s mission and long-term health. Safety & Mods Data Science operates as a closed-loop system: Safety Insights helps the organization understand, measure, and prioritize risk, while Safety Enforcement Data Science builds and optimizes the models, rules, and systems that detect and mitigate abuse effectively, efficiently, and fairly. We are looking for a Senior Data Scientist to join Safety Enforcement Data Science. You will own one or more defined enforcement problem areas and translate ambiguous abuse risks and written policy into measurable signals, labels, detection approaches, actioning logic, and evaluation plans. You will work closely with Product, Engineering, Machine Learning, Policy, Safety Operations, Threat Intelligence, and Safety Insights partners to turn analysis into scaled mitigation. This is a hands-on role. You will investigate adversarial behavior, prototype and evaluate models and rules, tune decision thresholds, define launch guardrails, diagnose enforcement gaps, and monitor systems after deployment. You will also help the team use modern AI methods—including language models—where they improve detection, investigations, labeling, or operational workflows, with rigorous evaluation and appropriate human oversight. This role may involve exposure to sensitive content and participation in an incident-response on-call rotation, including occasional work outside normal hours. Responsibilities: Own the enforcement lifecycle. Lead a defined abuse or enforcement problem from problem framing and signal discovery through labels and taxonomies, model or rule development, thresholding, rollout, monitoring, and iteration. Discover and characterize abuse. Analyze large-scale behavioral, content, account, and network data to identify adversarial patterns, emerging threats, and gaps in existing mitigations. Build and evaluate decision systems . Develop models, classifiers, scoring systems, rules, and analytical prototypes using the statistical, machine-learning, or GenAI methods best suited to the problem. Define decision-quality standards. Create evaluation frameworks that address precision, recall, calibration, coverage, robustness, fairness, user friction, reviewer capacity, and other operational costs—not model performance in isolation. Ship with engineering partners. Partner with Engineering and Safety ML to productionize promising approaches; define data and logging needs, launch guardrails, monitoring and alerting, escalation paths, and rollback criteria. Translate policy into systems. Work with Policy and Safety Operations to convert written policy and product requirements into modelable targets, labels, decision rules, reviewer workflows, and measurable enforcement outcomes. Measure real-world impact. Design staged rollouts, experiments, and observational analyses that test whether interventions reduce harm while managing false positives and downstream effects on users, communities, and operations. Respond and learn. Support investigations and urgent escalations, identify mitigation options, conduct post-incident analyses, and turn one-time findings into durable detection or monitoring improvements. Scale the team’s effectiveness. Build reusable datasets, evaluation harnesses, monitoring tools, and documentation; communicate clear recommendations to technical and non-technical partners; and mentor junior peers when opportunities arise. Stay ahead of the threat landscape. Track emerging abuse tactics and advances in detection, adversarial ML, language models, synthetic data, and human-in-the-loop safety systems, and apply them pragmatically. Required Qualifications: Education & Experience: Ph.D. with 4+ years OR Master’s degree with 8+ years* of relevant industry experience in Data Science, Applied Science, Economics, or a related quantitative role (e.g., Statistics, Computer Science, Operations Research). Applied experience in safety, enforcement engineering, fraud, or similar fast-paced, adversarial domains where data may be incomplete or evolving. Expertise in SQL and Python to write production-grade, reusable analytical code and contribute directly to data pipelines. Strong background in statistical analysis, experiment design, causal modeling, and ML techniques (e.g., classification, ranking, anomaly/pattern detection). Practical knowledge of evaluating real-world decision systems, including sampling, label quality, class imbalance, precision/recall, calibration, thresholding, drift, and monitoring. Proven ability to deconstruct ambiguous problems into scopable components, bias toward action, and drive impactful decisions alongside Product, Engineering, Operations, and Policy teams. Exceptional communication skills with demonstrated ability to convey complex technical concepts, tradeoffs, and recommendations to both technical and non-technical stakeholders. Preferred qualifications Experience in Trust & Safety, integrity, fraud, spam and abuse, account security, cybersecurity, risk, content moderation, or another adversarial domain. Experience with one or more enforcement-relevant methods such as graph or network analysis, NLP, embeddings, sequence or behavioral modeling, weak supervision, synthetic data, or human-in-the-loop evaluation. Experience applying language models to detection, investigations, labeling, reviewer assistance, or workflow automation—and measuring quality, cost, latency, and safety tradeoffs. Experience partnering with engineers to launch and monitor models or rules in production, including incident response and post-launch tuning. Experience designing prioritization or triage systems for human review operations, including analysis of reviewer quality and capacity. Experience assessing fairness, bias, privacy, and policy-aware tradeoffs in automated decision systems. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-SP1 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: $190,800 — $267,100 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: Croatia; Serbia; Germany; Austria; Zurich, Switzerland; Remote - Europe; Remote; Remote, Worldwide; Zagreb, Croatia Workplace: remote About Welo Global  Welo Global is a leader in multilingual AI, technology, and content solutions serving over 2,000 clients in 300 languages. The company combines globally scaled multilingual infrastructure, including a network of over 500,000 linguists and domain experts, with advanced NLP, computational linguistics, and best-in-class compliance backed by seven ISO certifications. Welo Global’s five brands—Welocalize (multilingual content and localization services for global enterprises), Park IP (intellectual property and patent translation services for law firms and corporate legal teams), Welo Life Sciences (regulated language and compliance-aligned content solutions for pharmaceutical, biotech, and medical device organizations), Adapt (multilingual performance-led digital marketing agency), and Welo Data (multilingual data generation, evaluation, and human data infrastructure for AI systems)—serve distinct customer segments with purpose-built expertise, fit-for-purpose solutions, and supporting technology. weloglobal.com  The Role  We are seeking an experienced English into Bosnian (Cyrillic) Review Lead to serve as the language quality owner and primary client-facing linguistic expert for a leading global technology client.  You will own the locale’s interpretation of the client’s quality standards and make sure that the Post-Editing Lead, Post Editors, and Reviewers all work to the same shared understanding of “good”. You will be the main point of contact for the client’s Language Manager on quality discussions, arbitration, and escalations, and you will drive consistency, quality governance, and continuous improvement across UI, marketing, product, informational, legal, and support content.  Project Details  - Location: Remote (based in one of the approved countries below) - Expected volume: Approximately 24 hours per month - Employment type: Freelance / Independent Contract - Task type: Language Lead Support - Content types: UI, Marketing, Informational, Legal, Product, Support - Language pair: English into Bosnian (Cyrillic) What You Will Do  -Analyse requirements: Review every style guide and glossary update, assess which quality categories and content types it affects, and record the changes. Track what the client’s Language Manager actually corrects and says in calibration and quality calls, not only what is written down. -Relay expectations: Run regular alignment roundtables with the language community and publish a structured monthly quality digest covering performance, priority improvement areas, style guide updates, and notable errors and arbitration outcomes. -Manage review quality actively: Coach Reviewers one-to-one using specific examples, run shadowing sessions, and deliver training built from real alignment data. -Improve the standard: Turn recurring justification patterns into well-reasoned style guide proposals, and share cross-locale patterns with the Welocalize Quality Manager. -Maintain alignment with the client: Treat Language Manager decisions as the standard, raise concerns with evidence at the right forum, and aim for fewer surprises over time. What You Will Own -The locale’s review service score, including RQE audit and alignment performance -The community’s shared understanding of quality  -The integrity of the arbitration record, ensuring that every arbitration sent to the client is valid and well-evidenced -The working relationship with the client’s Language Manager, including clarifications, escalations, and expectation tracking Requirements - Experience: 3 to 5+ years in Localization Quality Assurance (LQA), Senior Language Lead, or Quality Governance roles - Client and stakeholder management: Direct experience as the primary point of contact for client Language Managers, managing formal arbitration or escalation processes, and facilitating conflict resolution - Quality audit frameworks: Deep, practical experience with localization evaluation frameworks, including RQE, LQE, error rubrics, and severity mapping - Style guide and expectation analysis: Ability to analyse style guide changes, identify implicit client expectations, and map tolerance by content type (for example, Marketing vs. Legal vs. UI) - Meeting facilitation: Hands-on experience with one-to-one coaching, shadowing sessions, alignment roundtables, and structured monthly quality reporting - Analytical approach: Skill in tracking error concentrations, severity drift, and alignment scores, and in converting justification patterns into style guide improvements - Language: Native-level Bosnian (Cyrillic) and professional-level English - Preferred: Prior experience supporting major technology localization programs and working with calibration or alignment tools Additional Key Information  - Successful candidates will be provided with a dedicated device for project work. - A background check is required as part of onboarding. - Candidates must be physically located in one of the approved countries. Approved countries: Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa Rica, Croatia, Czech Republic, Denmark, Egypt, El Salvador, Finland, France, Germany, Ghana, Greece, Guatemala, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Kenya, Lithuania, Malaysia, Mexico, Netherlands, New Zealand, Nigeria, Norway, Peru, Philippines, Poland, Portugal, Qatar, Romania, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Arab Emirates, United Kingdom, United States and Vietnam.   Why Join Us  This role offers a long-term partnership with global teams on high-visibility products. You will benefit from steady project volume, comprehensive onboarding and ongoing upskilling, and access to established linguistic resources.  If this sounds like the right opportunity, we would love to hear from you.  Beware of recruitment scams. Welo Global and all of its brands never recruit through direct messages on social media or WhatsApp, and never asks candidates to pay money for a job. Official emails come only from @welocalize.com, @welodata.ai, or @hire.lever.co, and all applications go through jobs.lever.co/weloglobal. Be wary of unexpected offers, generic email addresses (such as Gmail or Yahoo), and requests for fees or sensitive information. Report suspicious messages and learn more: Recognizing Scams and Confirming Legitimate Welocalize Communications – Welo Global Job Reference: #LI-JC1

Развитие классификатора данных для полей БД (Tabular ML). Развитие существующей ML-модели (RandomForest), которая выявляет ПДн в полях баз данных на основе метаданных и значений.

Mistral is looking for an Applied AI Technical Lead Forward Deployed AI Engineer to lead enterprise AI solution delivery and customer deployments.

Location: Monterrey, Mexico; Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.BR, Brazil; Remote.CO, Colombia; Rosario, Remote.LATAM-country We are looking for a Senior AI Lead who will act as the primary AI consultant within the organization and help shape the vision, roadmap, and execution of AI initiatives. You will work across stakeholders and delivery teams to guide responsible AI adoption and establish a sustainable AI practice. The AI Practice is focused on building a sustainable AI capability within an enterprise environment and connecting AI initiatives to measurable business impact. It covers strategy, roadmap planning, solution delivery, governance, and adoption enablement across business units. Serve as the primary AI consultant within the organization to identify opportunities for AI driven innovation Develop and drive the AI strategy aligned with business goals Lead the assessment prioritization and roadmap planning of AI initiatives across business units Collaborate with product owners data scientists engineers and business stakeholders to scope design and deliver AI solutions Provide guidance on AI technologies frameworks and architectures including LLMs RAG AI agents prompt engineering and MLOps pipelines Ensure AI projects align with business objectives maintain technical quality and follow ethical AI principles Mentor AI team members to build internal capabilities and support knowledge sharing Track AI trends tools and emerging technologies to support continuous improvement and innovation Manage stakeholder expectations and communicate roadmap progress benefits risks and delivery outcomes Champion AI adoption through workshops demos and enablement sessions Define and track KPIs adoption metrics and ROI frameworks for AI initiatives Establish AI governance frameworks usage policies and guardrails for responsible AI adoption Guide the evolution toward an Agentic SDLC model across software development workflows Drive change management activities to support AI augmented and agent orchestrated ways of working 7+ years of experience in AI consulting solution architecture or related roles with leadership responsibilities Experience with AI and ML technologies including LLMs NLP computer vision conversational AI RAG architectures and MLOps Experience driving AI initiatives from ideation through design and implementation in enterprise environments Experience working with clients and executive leadership to translate business needs into AI solutions Hands on collaboration experience with development teams and data scientists across the AI and ML toolchain including Python PyTorch TensorFlow Jupyter Git and CI/CD pipelines Understanding of AI ethics data privacy and responsible AI principles Experience designing and delivering AI training enablement programs or internal advocacy initiatives that drive measurable adoption Familiarity with AI governance frameworks and organizational policy development for responsible AI use Understanding of Agentic SDLC methodologies and AI native delivery practices Advanced degree in Computer Science AI Data Science or a related field or equivalent professional experience Experience with cloud AI platforms such as AWS SageMaker Bedrock and Amazon Q Developer Experience with cloud AI platforms such as Azure OpenAI Service Azure AI Studio and Cognitive Services Experience with cloud AI platforms such as Google Cloud Vertex AI Gemini API and Document AI Experience with AI lifecycle management and MLOps platforms such as MLflow Kubeflow Weights & Biases and SageMaker Pipelines Experience with AI orchestration and agent frameworks such as LangChain LangGraph CrewAI AutoGen Semantic Kernel and Haystack Familiarity with vector databases and retrieval infrastructure such as Pinecone Weaviate Chroma Qdrant pgvector and Elasticsearch with kNN

​​​​Исследование и внедрение современных архитектур, а также подходов к обучению моделей. Разработка AI-агентов прогнозирования.

Разработка и обучение моделей машинного обучения для задач NLP и компьютерного зрения; Анализ и обработка данных;

Полный цикл разработки и внедрения ML-моделей в продакшен, мониторинг и ретренинг. Лидерство в реализации бизнес-задач.

Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision. Возможен также classic ML.

Location: London Workplace: Hybrid ABOUT BEAMERY With the rapid expansion of AI and automation, the future of work has never been more challenging for organizations. Beamery’s unique jobs, skills and tasks data platform helps organizations navigate these challenges and make more informed decisions across Talent Lifecycle Management. Our solutions power recruitment, mobility, upskilling, diversity, work architecture and workforce planning for some of the world’s most forward-thinking companies. We believe that where you work is much more than just a job. Millions of people are being left behind every day in their careers, and we’re on a mission to fix this by creating equal access to meaningful work, skills and careers for all. We are an equal opportunity employer committed to building a representative Beamery, creating an equitable, inclusive and engaging environment for our people. So, what are you waiting for? Join us and help us transform the future of work once and for all. What’s ahead — and why it’s an exciting time to join the team:  Building agentic AI with advanced orchestration (multi-agent workflows, tool use, evaluation loops) that turns complex talent data into decisions. - Training and fine-tuning our own models on unique data about jobs, skills and tasks, and benchmarking them against the best in our market. - Joining an award-winning AI team (ACM RecSys Challenge 2026) that turns new ideas into innovative solutions with scientific rigour. - Sharing our work and learning from industry peers through challenges, conferences and publications. But it’s not all about creating high-quality products, we also very much value the company culture we have worked hard to create; built on trust, empathy & honesty ensuring our workforce is able to bring their full selves to work. ABOUT THE TEAM The Data Science team builds the AI models behind Beamery's Data Foundry, the common data and AI layer powering all Beamery products, and works hand in hand with the product teams to empower their features with AI (such as Talent Match and recommendations). At the heart of our work is the Beamery taxonomy, the language of work (skills, role titles and tasks) that every Beamery product speaks, built on more than a decade of research and kept current by the Taxonomist, our agentic system, so it reflects the real pulse of the job market. We combine LLMs, Transformers and embedding models with classic machine learning, always choosing the best balance between quality, latency and cost. We see ourselves as pioneers in AI for HR Tech: in 2026 the team finished 1st in the industry track and 2nd overall out of 40 teams at the ACM RecSys Challenge. ABOUT THE ROLE You will join the team as a Senior Data Scientist and own our matching, recommendation and taxonomy mapping models, working closely with the product engineering teams that bring them to our customers. You will be responsible for the full cycle of your work, from defining how quality is measured and designing the models to running robust scientific experiments, tracking technical and business metrics, and writing and reviewing code. You will also advise the product teams on how to implement AI features with an evaluation-first mindset, and keep pushing the boundaries of Beamery's AI. KEY RESPONSIBILITIES - Build, improve and own our matching and recommendation models (e.g. candidate to job matching, internal mobility recommendations etc.). - Apply an evaluation-first mindset: build golden datasets from real recruiter and user decisions, define quality metrics, design and run robust scientific experiments, track and report both technical and business metrics (using LLM-as-a-judge and human feedback), and communicate results and metrics clearly to the product teams. - Write clean and tested Python code using AI coding agents (such as Claude Code or Codex), and review code with care, both from your peers and from AI agents, so the team's codebase stays consistent, readable and safe. - Follow recent research (e.g. LLMs, embeddings, recommender systems, explainability, model evaluation) and bring the most relevant ideas into our product roadmap. - Document your work and present it clearly to product, engineering and business stakeholders, and contribute to the team's research visibility (such as challenges, conferences and publications). REQUIRED SKILLS AND EXPERIENCE - A minimum of 2-4 years of experience as a Data Scientist, Senior Data Scientist (or a similar role) in an AI startup or an AI team, with a dynamic environment and a variety of use cases. - Strong experience with recommender systems, search, ranking or matching (e.g. learning-to-rank, scoring models, Gradient Boosting Trees, embedding models). - Fluency in evaluation: designing datasets and quality metrics (such as AUC, NDCG, precision and recall), and using LLM-as-a-judge and human feedback. - A strong foundation in NLP and GenAI, including Transformers, LLMs and embedding models (training or fine-tuning them is a strong plus). - High-quality software development skills in Python (clean design, testing, packaging, maintainable code etc.), practical knowledge of CI/CD, and hands-on experience with agentic coding, with the judgement to know when AI-generated code is right or wrong. - The ability to simplify complex problems, to work with ambiguity, and to collaborate with data scientists, engineers, product managers and business stakeholders. - A Master's degree or PhD, or equivalent experience, in Computer Science, Data Science, Mathematics or a related field. NICE TO HAVE - Experience with explainable or conversational recommendations. - Experience in HR Tech, talent acquisition or workforce data. - Publications, open-source contributions or strong results in ML challenges (e.g. RecSys, Kaggle). WHAT WE OFFER - The chance to own core AI models that are used by some of the world's most forward-thinking organisations. - A team that publishes, competes and is recognised in the research community, while shipping real products. - Work with the latest AI technologies and tools, including AI coding agents as part of the daily workflow. - Close collaboration with experienced data scientists, QA, platform and product engineers. At this time, we are unable to provide Visa sponsorship for this role. Beamery is for Everybody. Diversity and open expression are fundamental to us. We acknowledge the challenges in our industry and strive to develop an inclusive culture where everybody can contribute. We are dedicated to creating an inclusive environment for everyone, regardless of ethnicity, religion, color, sexual orientation, gender identity, race, national origin, age, disability status, or caregiver status. If, for whatever reason, you need us to make reasonable adjustments and adaptations to our recruitment process, please email accommodations@beamery.com Visit our Diversity, Equality and Inclusion page https://careers.beamery.com/life-at-beamery/diversity-equality-inclusion/to learn more about progress and commitments.

Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision. Возможен также classic ML;

Вакансия предполагает создание и внедрение нейросетевых моделей, которые объединяют методы компьютерного зрения (CV) и обработки естественного языка (NLP). Основная задача — разработка эффективных алгоритмов и моделей, способных работать на пересечении этих двух областей машинного обучения.

Разрабатывать и исследовать ML-модели для генерации изображений и визуализации интерьерных/строительных решений на основе товаров Лемана ПРО. Проектировать и реализовывать end-to-end пайплайны для PoC: от сбора и подготовки данных до обучения, оценки и демонстрации результатов.

- Сильный опыт в ML, Deep Learning, NLP и LLM. - Практический опыт создания AI-продуктов, работающих в production и в большом масштабе.

Location: Москва Команда управления развития искусственного интеллекта (блок «Развитие клиентского опыта B2C») ищет NLP/LLM-инженера для развития AI-copilot премиальных клиентских менеджеров. Мы переходим от классических NLP-решений к адаптивным мультиагентным системам, способным самостоятельно решать задачи в контуре бизнеса: поиск по базе знаний, суммаризация профиля, извлечение инсайтов из звонков, управление графиком, проактивные подсказки на звонке. Ищем Senior-инженера, который возьмет на себя архитектурную ответственность за решения с LLM под капотом, выстроит процессы оценки качества и будет писать чистый, поддерживаемый код, готовый к интеграции в продакшн выделенной бэкенд-командой. Обязанности * архитектура и разработка: проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы и автономных AI-агентов с использованием планирования, function calling и мультиагентной оркестрации * дообучение и адаптация: SFT/RLHF/DPO, LoRA/QLoRA под специфичные бизнес-задачи. Принимать решения о том, когда дообучение необходимо, а когда достаточно промпт-инжиниринга или RAG * система оценки (Eval): поддерживать культуру качества, внедряя продуктовые и ML-метрики (RAGAS, DeepEval, кастомные LLM-as-a-Judge), автоматизировать регрессионное тестирование, связывать технические метрики с бизнес метриками * качество кода и интеграция: писать чистый, хорошо документированный и тестируемый python-код, который легко интегрируется и поддерживается бэкенд командой. Формировать стандарты разработки в рамках DS экспериментов и проектировать API-контракты для ML-компонент * менторство и влияние: участвовать в код-ревью, формировать технические стандарты команды, декомпозировать бизнес-задачи и предлагать оптимальные ML-решения. Работать напрямую с продуктом и стейкхолдерами. Требования * опыт в DL/NLP от 4 лет включая обучение/дообучение моделей на PyTorch, из них не менее года практического опыта с LLM * практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain/LangGraph * глубокое понимание архитектуры современных LLM, включая механизмы attention, MoE, RoPE, KV-cache, методы alignment (RLHF/DPO), принципы эффективного инференса * сильная инженерная культура: Python (asyncio, pydantic, fastapi), написание чистого тестируемого кода (pytest), опыт разработки асинхронных микросервисов. Понимание Docker/K8s, CI/CD для ML * оценка и мониторинг: уверенное владение инструментами LLMOps (Arize, LangSmith, W&B, Phoenix). Опыт создания кастомных eval-пайплайнов и понимания того, как метрики влияют на продукт * работа с данными: опыт предобработки сложных данных для подготовки датасетов, знание SQL * автономность и коммуникация: Умение переводить неопределённые бизнес-задачи в технический план, аргументированно отстаивать архитектурные решения, работать без микроменеджмента. будет плюсом: * опыт опромышливания AI-агентов и знание принципов LLMOps, включая CI/CD, оркестрацию и управление версиями промптов * опыт интеграции ML-решений в высоконагруженные системы * понимание безопасности LLM-приложений. Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы после испытательного срока * 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.