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Location: Paris Workplace: OnSite ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. The Role As a Research Engineer on the Robotics team, you will deploy and refine state-of-the-art AI models for mobile manipulation on real robots. This role exists to bridge the gap between cutting-edge research and real-world robotic applications, ensuring that Mistral’s in-house models are not only theoretically sound but also practical, scalable, and effective in dynamic environments. You will join a team of engineers and researchers dedicated to pushing the boundaries of what robots can achieve. Your work will directly impact the development of autonomous systems, enabling them to operate reliably in complex, real-world scenarios. The scope of this role spans from architecting data pipelines for model training to maintaining fleets of diverse robots, all while collaborating closely with cross-functional teams to validate and iterate on robotic systems. What You Will Do - Deploy state-of-the-art AI models for mobile manipulation on real robots in production environments. - Architect and optimize data pipelines to support the training of advanced robotics models on large-scale datasets. - Set up, maintain, and scale fleets of robots, ensuring their operational readiness and reliability. - Design and conduct experiments to validate robotic systems in real-world conditions. - Collaborate with engineers and researchers to refine models and improve system performance. What We're Looking For - Hands-on experience developing software for real-world robotics applications. - Mastery of Python and a strong background as a software developer. - High engineering competence, with the ability to design complex systems and deploy them in production. - A self-starter mindset, with the autonomy to drive projects forward and the adaptability to collaborate effectively. - Experience with robotic control frameworks, such as ROS, and proficiency in sensor integration and actuator control. - Knowledge of control theory, machine learning, or computer vision as applied to robotics. - Experience with robotics simulators, hardware development, or maintaining large codebases. - A proactive approach, with a focus on delivering results and continuously improving systems.   WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: London Workplace: Hybrid ABOUT NIANTIC SPATIAL We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. ABOUT THE ROLE We are looking for a Research Engineer to help develop our Geospatial Visual Localization technologies, which allows people and robots to accurately localize themselves anywhere on the planet. This is an end-to-end applied research role. You will develop new approaches to open problems alongside our researchers in 3D vision and machine learning, and publish your work at leading conferences. You will help build and train large models on large-scale multi-modal datasets, including our proprietary collection of 30 billion posed images and ship them into production, to be used by people and robots from all over the world. Our team works with heterogeneous captures from phones and other handheld devices, 360 cameras, vehicles, and drones, as well as aerial and satellite imagery, LiDAR, and external geographic data. Your focus will be to improve accuracy and robustness across urban and rural locations, cameras, seasons, weather, and capture quality. The system must remain reliable with noisy GPS, in GPS-denied environments, and with outdated reference maps. Each pose estimate comes with an uncertainty that our customers rely on, and keeping it well calibrated across these conditions is part of the work. WHAT YOU'LL DO - Design, implement, and evaluate novel visual localization algorithms, combining 3D vision and deep learning. - Train large models on multi-modal data at scale, and build the benchmarks that measure their accuracy and robustness. - Develop robust solutions for difficult real-world conditions: noisy or denied GPS, changing appearance, varying capture quality, large geographic extents, and imperfect reference maps. - Work side-by-side with leading researchers in visual localization, 3D computer vision, and mapping, as well as engineering and product teams, to identify the highest-impact problems and turn research into end-to-end solutions that operate at production scale. - Communicate results clearly through technical writing, design reviews, internal presentations, and publications. WHAT YOU'LL BRING - Strong grounding in 3D computer vision, deep learning, robotics, photogrammetry, or a closely related field. - Demonstrated experience with multi-view geometry and related problems, such as camera pose estimation, monocular depth estimation, SfM, SLAM, georeferencing, point-cloud registration, or sensor fusion. - Knowledge of transformer models for 3D vision, such as vision foundation models, feed-forward 3D reconstruction, or multi-view and multi-modal attention. - Knowledge of geospatial data and coordinate reference systems. - Ability to formulate ambiguous real-world problems, choose appropriate mathematical or learning-based tools, and evaluate solutions rigorously. - Strong Python skills and experience training deep learning models at scale, with libraries such as PyTorch, NumPy, and SciPy. - Strong software engineering skills, with the ability to turn research prototypes into reliable, scalable, and maintainable systems. - Experience taking an algorithm from research idea and experimental prototype through evaluation, benchmarking, and deployment on real-world data. - Clear written and verbal communication and the ability to collaborate across research and engineering disciplines. NICE TO HAVE - A PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field, or equivalent research or industry experience. A PhD is not required. - A strong publication record or substantial active involvement in the research community, especially at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, SIGGRAPH Asia, 3DV, ICRA, RSS, or related conferences and journals. - Experience with aerial or satellite imagery, GNSS/GPS, IMUs, LiDAR data, geodesy, mapping systems, or large-scale spatial data. - Evidence of technical leadership, mentorship, or successful cross-functional delivery. COMPENSATION & BENEFITS Base salary: Competitive market rate. Compensation also includes an annual bonus, equity, and a comprehensive benefits package. LOCATION & WORK MODEL This role is based in our London office, with three days per week in office. INCLUSIVE APPLICATION We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. EQUAL OPPORTUNITY Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. CANDIDATE PRIVACY I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE The Robotics Applied Research Lead establishes and leads Humanoid’s AI research and engineering function. You decide which Physical AI data problems we work on, define how we test them, and stay directly involved in the most important technical work. You connect four areas that often sit apart: 1. Multimodal and embodied data 2. Transformer-based models and how data impacts robot policies 3. Rigorous offline and real-world evaluation of policies 4. Deployment of models in the data ecosystem This is a player-coach role. In the first year, at least half of your time is direct technical work: designing models and experiments, reviewing or writing code, inspecting data, debugging training, examining failures, and reviewing robot rollouts. You will also build a small research and engineering team. Add people only when the work requires them. MANDATE Turn Humanoid data and technical access into measurable advances in Physical AI systems. You own the answers, and the evidence, to questions such as: - Which model families should we train, adapt, or evaluate? - Which data produces meaningful gains in robot performance? - What coverage, sensors, annotations, and quality controls do the models require? - Which offline measurements predict real-world robot performance? - Where should we adapt existing open models rather than train from scratch? - What should Humanoid build itself, and what should it reuse or obtain through partners?   WHAT YOU'LL DO - Set the technical direction: Define a focused research and engineering roadmap. Select a small number of high-value bets with clear hypotheses, baselines, milestones, and stop criteria. Decide what we build, adapt, license, or access through partners. Drop work that no longer has a strong case. Likely scope includes vision-language-action models, multimodal Transformers, robot foundation models, action representation, imitation and reinforcement learning, world models, cross-embodiment transfer, data-efficient adaptation, and robot-policy evaluation. - Own Transformer research and engineering: Design, adapt, train, and evaluate Transformer-based systems for embodied tasks. Make the architecture and training decisions yourself. Start from strong existing models and baselines. Train from scratch only when evidence supports the cost. Build or review the critical code. - Connect models to data strategy: Define the data needed to train and evaluate the selected models. Specify sensors, modalities, annotations, mixtures, and quality controls. Measure the effect of data quality, diversity, and composition on model behavior. Distinguish data volume from data value. You set technical data requirements; you do not run annotation workforces or field collection. - Own evaluation: Establish offline and real-world evaluation systems, baselines, held-out conditions, and release gates. Guard against leakage, overfitting, and weak success definitions. Compare offline metrics with real-robot outcomes. A benchmark score alone is not a successful result. The evaluation must support a decision. - Move work onto physical systems: Take projects from problem definition through training, hardware integration, and real-world testing. Design safe test plans and staged deployment. Analyse failures across data, perception, model, control, hardware, and environment. Validate material claims on physical systems. - Build a small technical team: Recruit a few complementary researchers and engineers. Lead architecture, experiment, code, and failure reviews. Keep the team focused on validated results, not paper count, benchmark theatre, or headcount. - Represent the function and own responsible development: Explain the strategy to customers, partners, and investors. Convert customer needs into testable technical requirements. State uncertainty and limits clearly. Define safety constraints, supervision, and rollback paths for physical trials. Require reproducible evidence before making capability or safety claims.   WHAT WE'RE LOOKING FOR - Deep, direct Transformer expertise: This is a hard requirement. You must have personally made material architecture or training decisions in one or more substantial Transformer-based systems, such as vision Transformers, VLMs, VLAs, multimodal foundation models, Decision Transformers, diffusion Transformers, video Transformers, world models, or Transformer-based perception, planning, or control. You should be able to explain, in detail, how you represented inputs and outputs, tokenized data, fused modalities, structured attention and temporal context, chose losses, built the data mixture, distributed and monitored training, diagnosed failures, and what you personally changed and why it affected task performance. Using hosted model APIs, prompting language models, or running an unchanged public training recipe does not meet this requirement. - Physical AI or embodied-system experience: You have worked on machine learning for a system that perceives or acts in the physical world: robotics, autonomous vehicles, drones, industrial automation, manipulation, mobile robots, humanoids, wearable or egocentric systems, or similar. At least one substantial project progressed beyond an offline dataset into a real or operational physical system. Simulation-only experience is not sufficient unless you also owned credible sim-to-real transfer and physical validation. - Hands-on technical ability: You write and review high-quality Python. You work directly with PyTorch or an equivalent framework. You can read unfamiliar model and training code, design and debug experiments, inspect failures and rollout traces, work with large multimodal datasets, and make informed decisions about compute, memory, latency, and cost. - Technical leadership: You have set direction for important technical work: a research or engineering group, a major model or robotics programme, or a cross-functional project you owned. You have hired or mentored researchers or engineers, made architecture and resource decisions, stopped weak work, and taken a result into deployment. A management title is not required. Direct ownership is. - Evaluation, communication, and judgment: You design held-out tests, run controlled comparisons, find leakage and confounds, and report results with appropriate uncertainty. You separate evidence from belief, state what remains unknown, and reduce broad research questions to testable work. - Strong preference - Computer Vision: We are particularly interested in candidates who combine deep expertise in Transformer architectures with a strong background in computer vision. Relevant experience may include visual representation learning, vision-language models (VLMs), video and temporal modelling, object detection, segmentation, tracking, multi-view and egocentric vision, 3D and spatial reasoning, pose estimation, calibration, localisation, sensor fusion, or real-time vision systems. - Education: A PhD in machine learning, computer vision, robotics, computer science or a related field is valuable but not required. An equivalent record of original model work, technical leadership, and real-system delivery is sufficient. We care more about what you personally designed, trained, tested, and deployed than about a specific degree or number of years. NICE-TO-HAVE: - Technical expertise: Experience with VLA or robotic foundation models, action generation, diffusion or flow-matching policies, imitation learning, reinforcement learning, world models, robotic manipulation, cross-embodiment training, sim-to-real transfer, distributed training, ROS 2, or safety-critical systems. - Additional experience: Research publications, open-source contributions, early-stage company experience, or direct collaboration with technical customers and research partners.   WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

KPK is looking for a Quantitative Research Graduate to build risk simulations, analyse onchain data, and model portfolio risk and AMM vault strategies.

Location: Amman, Jordan We are looking for a Marketing Database & Research Coordinator who will manage, maintain, and enrich our marketing database, ensuring data accuracy, consistency, effective segmentation, and campaign targeting. The role also involves conducting regional market research, identifying relevant industry events, assessing market opportunities, tracking trends, and monitoring developments across different markets. Responsibilities Database Management Define and enforce data standards across the database, including naming conventions, field formats, job title and industry classifications, country and city values, and phone and email formats. Standardize incoming records so every entry follows the same structure, whatever its source. Find and merge duplicate records, and resolve conflicting information between entries. Maintain data entry guidelines and ensure consistency. Acquiring New Contacts Find and add new contacts and accounts that match the company's target profiles by industry, company size, seniority, and region. Source contacts through company websites, industry directories, association member lists, event attendee and exhibitor lists, and enrichment platforms. Capture and process leads from events, webinars, campaigns, and inbound channels, and validate them before they enter the marketing database. Build targeted contact lists by region and segment to support campaigns and sales outreach. Track contact acquisition against targets for each region. Keeping Data Up to Date Verify and refresh existing records on a regular schedule, updating job changes, company changes, new decision-makers, and contact details. Detect and fix outdated, bounced, or invalid contacts, and archive records that are no longer relevant. Enrich incomplete records with missing fields such as title, department, company size, and location. Run periodic data health audits and report on completeness, accuracy, and freshness. Monitor news and announcements such as mergers, acquisitions, leadership changes, and closures, and reflect them in the database. Regional Events Research Research and maintain a regional calendar of relevant industry events, including conferences, exhibitions, trade shows, summits, and networking forums. Evaluate each event's relevance using audience profile, attendee seniority, exhibitors, sponsors, past attendance, and cost. Recommend events to attend, sponsor, or exhibit at, with a short rationale for each. Collect speaker, exhibitor, and attendee information from events to feed new contacts into the database. Monitoring Regional Developments Continuously monitor developments in each region that could affect the business, including regulatory changes, government initiatives, economic conditions, major projects, tenders, and industry shifts. Set up news alerts and tracking sources for each region. Flag significant developments to relevant stakeholders promptly, with a short assessment of likely impact. Requirements A bachelor's degree in Business, Marketing, Economics, Information Systems, or a related field. 1-2 years of experience in database management, lead research, or market research. Advanced Excel skills, including lookups, pivot tables, data validation, conditional formatting, and Power Query. Hands-on CRM experience including imports, deduplication, and data maintenance. Proficiency with contact sourcing and enrichment tools. Experience with market research sources, industry reports, and government and trade publications. Experience with a reporting or BI tool is a plus. Exceptional attention to detail and a disciplined approach to data consistency. Strong research skills and the ability to judge whether a source is reliable. Strong organization and the ability to manage recurring tasks across several regions. Awareness of data privacy regulations such as GDPR and local data protection laws. Professional fluency in English.

Location: Brussels; London; London OR Brussels Workplace: OnSite DeFi Quant Research Intern - Digital Assets Market Making Since 2017, Keyrock has been a change-maker in digital assets. We're a global, remote-first team of 200+ people across 40+ nationalities, with hubs in Brussels, London, Paris and New York.   We provide liquidity across 80+ centralized and decentralized exchanges - a position cemented by our Series C, led by SC Ventures. Today we run a full spectrum of capital markets businesses across centralized and on-chain venues: market making, high-frequency trading, options, OTC, DeFi trading desks, advisory, and asset management.   We're building toward the agentic investment banking capabilities for the era of digital finance. A proprietary agentic layer runs the mechanics of the market, so our people can focus on the work code cannot do: the deal, the relationship, and the judgement. We're not just envisioning the future of finance - we're building it. ABOUT THE INTERNSHIP PROGRAM Our internship programme gives early-career talent the opportunity to build practical skills, contribute to meaningful projects and learn alongside experienced colleagues in digital finance. Typically lasting six months and based in our Brussels office (London possible), each internship includes hands-on experience, clear learning goals and dedicated guidance and regular feedback. We look for curiosity, relevant foundational skills, deep, active interest and passion for digital assets and the potential to grow exponentially with us - you don’t need to arrive with all the answers. Where performance, business needs and available opportunities align, an internship may lead to a permanent role, though this is not guaranteed. We are looking for 3-4 high-caliber Quantitative Analyst Interns to sit within our Strategic Market Making (CeFi and DeFi), Credit and Corporate Development desks/teams (see other openings on our career page for details). Application deadline: 30 November 2026 Start date: 1 March 2027 Selection process: - Interview with Talent Acquisition representative - Interview with Hiring Manager - Recruitment Challenge The role About Strategic Market Making Desk Our Strategic Proprietary Market Making team researches and deploys systematic trading strategies across digital asset markets. Within the DeFi track, we focus on how blockchain mechanics, protocol design and on-chain market structure shape liquidity, execution and risk. As a DeFi Quant Research Intern, you will work alongside quantitative researchers, traders and engineers to study on-chain markets and support the development of data-driven trading strategies. You will analyse blockchain and market data, test hypotheses through backtesting and simulation, build research tooling, and investigate how real-world execution—including gas, transaction ordering, liquidity fragmentation and MEV—affects strategy performance.   This is a technical and applied internship. We are looking for someone who combines strong quantitative reasoning and coding skills with genuine hands-on DeFi experience. You should be curious about how protocols work beneath the interface and able to connect blockchain mechanics to market behaviour and trading outcomes. Key Responsibilities - DeFi Quant Research Intern - Research on-chain market structure by analysing liquidity, price formation, participant behaviour and execution across decentralized markets. - Study how protocol mechanics, transaction ordering, gas, slippage, latency and MEV affect execution quality and strategy performance. - Formulate trading hypotheses and evaluate them using blockchain data, market data, backtests and simulations. - Build reusable Python tools and data pipelines for research, monitoring and performance analysis. - Analyse relationships between centralized and decentralized markets and identify their implications for pricing, hedging and risk. - Support live strategies through monitoring, post-trade analysis and investigation of unexpected performance. - Present findings clearly, including assumptions, limitations, risks and recommended next steps. WHO YOU ARE/KEY REQUIREMENTS - DEFI QUANT RESEARCH INTERN - Quantitative and coding foundations: You are pursuing or recently completed a degree in mathematics, computer science, engineering, physics, quantitative finance or a related field. You are proficient in Python for data analysis and comfortable with SQL, Git and command-line workflows. - Practical blockchain understanding: You understand transactions, state changes, gas, blocks, consensus and finality at a working level, and can reason about how smart-contract interactions execute on-chain. - DeFi market knowledge: You understand AMMs, concentrated liquidity, swaps, liquidity provision, oracles and liquidations, and how decentralized execution differs from a centralized order book. - MEV awareness: You understand how transaction ordering and block construction can create arbitrage, liquidation, backrunning and sandwiching opportunities or risks, and how these dynamics affect execution. - Hands-on curiosity: You have used DeFi beyond coursework or social media. Ideally, you have built or analysed something yourself—such as an on-chain dataset, trading or monitoring script, AMM simulation, protocol integration or research project. - Trading judgement: You naturally question whether an apparent edge survives fees, gas, latency, slippage, adverse selection and changing market conditions. - Ownership and communication: You can structure an ambiguous problem, test assumptions and communicate findings and limitations clearly in English.   NICE TO HAVE - Experience querying blockchain data through RPCs, indexers, subgraphs or analytics platforms. - Ability to read Solidity, contract interfaces and event logs. - Exposure to Rust or another systems programming language. - Familiarity with non-EVM ecosystems or Layer 2 execution models. - A technical project demonstrating sustained interest in DeFi or on-chain markets. Our offer - A 6 month paid internship at world-renowned digital assets company with accommodation provided for entirety or part of it - The opportunity to learn about digital assets and blockchain from a 30,000 ft view of the entire ecosystem - Working from one of Brussels newest, nicest and most advanced office spaces - A global, collaborative and tech-driven environment. - Autonomy & Impact - our flat structure means you can contribute your ideas from day one and help shape the foundation that will power Keyrock’s future business. - Culture of Excellence - we value nimbleness, drive for success, owner mentality and collaboration. - Opportunity to leave your mark and start your career in one of the most reputable companies in the digital assets world - We are envisioning for the internship to be based pre-dominantly in Brussels. It is possible for a selected candidate to be based in London, UK, provided at least 1-2 months onboarding period would be spent in Brussels together with the rest of the interns cohort.   Our recruitment philosophy We value self-awareness and powerful communication skills in our recruitment process. We seek fiercely passionate people who understand themselves and their career goals. We're after those with the right skills and a conscious choice to join our field. The perfect fit? A crypto enthusiast who’s driven, collaborative, acts with ownership and delivers solid, scalable outcomes. As an employer we are committed to building a positive and collaborative work environment. We welcome employees of all backgrounds, and hire, reward and promote entirely based on merit and performance. Due to the nature of our business and external requirements, we perform background checks on all potential employees, passing which is a prerequisite to join Keyrock. https://keyrock.com/careers/

Location: Москва Команда текстовых диффузионных моделей занимается прикладными исследованиями в области языковых моделей с альтернативными архитектурами и подходами к генерации текста: диффузионные текстовые модели, креативный генеративный ИИ. Обязанности * Организация SFT и RL дообучения моделей для агентного креативного ИИ * Улучшение моделей спекулятивного декодирования для ускорения инференса LLM, доработка архитектур, пайплайна обучения и протокола оценки. * Подготовка датасет для агентного сценария генерации русскоязычной поэзии. Требования * Владение инструментами и экосистемой: Linux, Python, torch, transformers, vllm. * Практический опыт проведения экспериментов в ML: планирование, сбор обучающих данных, оценка моделей, включая человеческую разметку, статобработка результатов, масштабирование. * Опыт работы с языковыми моделями: архитектурные модификации трансформера, распределенная тренировка, бенчмаркинг, оптимизация инференса. * Опыт работы с данными для обучения LLM: генерация синтетики, очистка данных, подготовка датасетов для обучения. **Будет плюсом** * Практический опыт запуска распределенных задач в вычислительном кластере. * Опыт научных публикаций по темам CS и ML. * Опыт доведения ML решений до прода. 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.

Location: Москва Центр Исследований занимается созданием SoTA технологий искусственного интеллекта для анализа финансовых данных и их применения для AI-трансформации Блока Риски. Наша задача разработать и обучить мультимодальную reasoning модель, объединяющей текстовые данные и цифровые следы клиентов для улучшения качества решений банковских задач. Обязанности * проводить генерацию и очистку синтетических данных с использованием LLM и LLM-as-a-Judge подходов * обучать большую мультимодальную LLM (текст + цифровые следы клиентов) * применять RL подходы для улучшения reasoning способностей модели * выполнять валидацию на современных бенчмарках разработанного решения с точки зрения точности и корректности рассуждений * оценивать решения на real-world данных. **Железо и данные:** * выделенный кластер 500+ GPU A100 80GB * Spark/Hadoop для работы с Big Data * доступ ко всем банковским данным, необходимым для построения решения. Требования * опыт работы от 3-х лет * глубокие знания ML/DL (трансформеры, большие языковые модели) * уверенный опыт обучения LLM моделей * уверенные знания Python: torch, transformers, peft * Английский B2+. **Будет плюсом:** * опыт работы с мультимодальными моделями * знание методов RL для LLM (RLHF, DPO, GRPO, RLVR, GSPO) * опыт распределенного обучения больших моделей (FSDP, ZeRO, Mixed-precision, Tensor/Pipeline Parallelism) * публикации в топовых журналах (Q1) или конференциях (A-A\*) * общее понимание современных ИИ-инструментов и их возможностей. Условия * комфортный современный офис м. Кутузовский пр. или м. Волгоградский проспект * **формат работы - офис** * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте (для вакансий уровня Junior) * 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 Наша команда создает real-time алгоритмы State Estimation для мобильных роботов (Visual/Lidar/Inertial Odometry, Sensor Calibration, SLAM) и разрабатывает менеджер мультимодальных карт — автоматическое выделение подкарт, выравнивание, сшивание. Наш стек: Linux, Git, Bash, Docker, ROS2; numpy, scipy, opencv, cupy, pytorch, ONNX, TensorRT, pcl, eigen. Обязанности * полный цикл разработки (Python/C++ ~50/50) * pip/uv пакеты и C++ библиотеки * встраивание в CI/CD * оптимизация ресурсов * тесты на реальных роботах * участие в архитектуре всей системы. Требования * опыт в Odometry / Sensor Calibration / Legged Robots State Estimation / SLAM (хотя бы что-то одно) * горите разработкой, ресёчем, математикой, робототехникой * сильный Python и/или C++: PEP8, SOLID, чистая архитектура, ООП, декораторы, чувство цены операций * умеете превратить научную статью в рабочий код; матан/линал/геометрия — «вспомню и применю» **Будет плюсом:** Place Recognition, научные статьи, фильтр Калмана / Factor Graph, работа с камерами/лидарами/IMU, работа c Nvidia Jetson, многопоточность, x86/arm64, PTP/NTP, k3s. Условия * комфортный современный офис возле МЦК ЗИЛ * ежегодный пересмотр зарплаты и годовую премию * уникальную систему обучения Сбера для профессионального и карьерного развития * расширенный ДМС и льготное страхование для семьи * гибкий дисконт по ипотечному кредиту, равный 1/3 от ключевой ставки ЦБ * подписка "Прайм" с возможностью использования на трех близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: London, United Kingdom Workplace: remote We are looking for a Lead Applied Researcher — Pricing to design and evolve dynamic pricing systems for our marketplace. You will develop pricing as a feedback-driven decision system , balancing supply, demand, and business outcomes under uncertainty and delayed signals. Unlike traditional surge-based pricing, our marketplace operates on a unique bid-based pricing model , where prices are shaped through direct negotiation between riders and drivers. This creates a fundamentally different optimization problem, requiring advanced approaches to modeling user behavior, incentives, and marketplace dynamics. Key Responsibilities Design and improve dynamic pricing policies, including elasticity and supply/demand response modeling Frame pricing as a decision system under uncertainty and delayed feedback Develop, test, and deploy real-time pricing algorithms Design and analyze A/B experiments to evaluate pricing impact Productionize models in collaboration with engineering, ensuring scalability and robustness Monitor system performance, including stability, feedback effects, and long-term behavior Contribute to methodological choices (e.g., optimization, control, RL-inspired approaches) Skills, Knowledge and Expertise Professional experience in Data Science, Applied Research, or algorithmic product optimization, preferably in marketplaces or pricing systems Strong background in statistics, optimization, and causal inference Proven ability to move from problem formulation to production-grade solutions Proficiency in Python, SQL, and experience with production ML systems Nice to Have Experience with economic modeling (elasticity, auctions, policy design) Familiarity with control systems, RL, or online optimization Experience working with real-time, high-load systems Why join us Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 47 countries Develop your professional skills with access to mentoring, career consulting, and learning programs Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects Work alongside people who take initiative, speak openly, and challenge themselves to grow Improve your language skills through co-financed courses and internal speaking clubs Final benefits may vary depending on the location.

Location: Limassol, Cyprus Workplace: hybrid We are looking for a Lead Applied Researcher — Pricing to design and evolve dynamic pricing systems for our marketplace. You will develop pricing as a feedback-driven decision system , balancing supply, demand, and business outcomes under uncertainty and delayed signals. Unlike traditional surge-based pricing, our marketplace operates on a unique bid-based pricing model , where prices are shaped through direct negotiation between riders and drivers. This creates a fundamentally different optimization problem, requiring advanced approaches to modeling user behavior, incentives, and marketplace dynamics. Key Responsibilities Design and improve dynamic pricing policies, including elasticity and supply/demand response modeling Frame pricing as a decision system under uncertainty and delayed feedback Develop, test, and deploy real-time pricing algorithms Design and analyze A/B experiments to evaluate pricing impact Productionize models in collaboration with engineering, ensuring scalability and robustness Monitor system performance, including stability, feedback effects, and long-term behavior Contribute to methodological choices (e.g., optimization, control, RL-inspired approaches) Skills, Knowledge and Expertise Professional experience in Data Science, Applied Research, or algorithmic product optimization, preferably in marketplaces or pricing systems Strong background in statistics, optimization, and causal inference Proven ability to move from problem formulation to production-grade solutions Proficiency in Python, SQL, and experience with production ML systems Nice to Have Experience with economic modeling (elasticity, auctions, policy design) Familiarity with control systems, RL, or online optimization Experience working with real-time, high-load systems Why join us Help us challenge injustice by creating fair choices for millions of people across in 47 countries Develop your professional skills with access to mentoring, career consulting, and learning programs Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects Work alongside people who take initiative, speak openly, and challenge themselves to grow Improve your language skills through co-financed courses and internal speaking clubs Annual salary from EUR 80 200 gross Final benefits may vary depending on the location.

Ищем сильного ML Researcher уровня Senior / Staff для сложной исследовательской задачи на стыке probabilistic ML, sequential decision making и моделирования поведения агентов.

Prime Intellect is looking for an Applied Research Lead to lead applied research programs, recruit researchers, and establish technical standards.

Prime Intellect is looking for a Research Engineer RL Infrastructure to build and optimize systems for large-scale reinforcement learning and distributed training.

Location: India; Australia; Japan; Singapore; Philippines; New Zealand About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Role We are seeking a highly skilled Machine Learning Research & Engineering Specialist to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, and performance optimization. This is a coding-intensive role requiring hands-on experience with Python, machine learning engineering, and coding agents integrated into development workflows. You will build, modify, debug, and validate machine learning implementations, create reproducible training and inference pipelines, and optimize systems for correctness, memory usage, and throughput. The ideal candidate combines strong quantitative foundations with practical coding skills and experience solving complex machine learning engineering problems. SENIORITY: Experienced / Research-Level Engineer EDUCATION: Master's degree or PhD in a relevant quantitative discipline LANGUAGE: Strong written and verbal English LOCATION: Remote — Worldwide Key Responsibilities Develop, implement, and validate machine learning models, training pipelines, inference systems, and supporting infrastructure. Implement or modify model components, data pipelines, evaluation systems, and numerical methods using Python. Build reproducible programmatic workflows using Python, command-line tools, and relevant machine learning frameworks. Debug numerical, algorithmic, and system-level failures to identify root causes and implement reliable solutions. Optimize model training and inference for memory usage, computational efficiency, throughput, and performance. Design and execute tests to verify implementation correctness and compliance with objective performance requirements. Review and validate challenging machine learning engineering tasks, including existing code and proposed solutions. Use coding agents as part of the development workflow to accelerate implementation, debugging, testing, and code review while validating generated results. Document technical approaches, implementation decisions, test results, and performance findings clearly. Deliver high-quality, reproducible task submissions within project deadlines. Requirements Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline. Strong professional or research experience in machine learning and practical ML engineering. Strong Python programming skills, including the ability to implement, debug, test, and optimize non-trivial code. Hands-on experience using coding agents within a Python development workflow. Understanding of machine learning model development, training and inference workflows, and evaluation methods. Ability to build reproducible implementations and verify correctness through systematic testing. Strong quantitative reasoning and problem-solving skills, particularly when diagnosing numerical or computational issues. Ability to work independently, manage technical tasks, and meet defined quality and submission requirements. Strong written communication skills and the ability to explain technical findings. Availability to contribute approximately 15 hours per week and begin the first task within 24–48 hours after onboarding.

Location: London, United Kingdom Workplace: hybrid Senior Applied Researcher will own well-defined marketplace efficiency problems end-to-end, from problem formalisation through modelling, deployment, and production monitoring. You will operate independently on complex problem areas, make explicit trade-offs between competing objectives, and partner closely with engineering to deliver robust, scalable solutions. Key Responsibilities Conduct in-depth market research and analyze trends to identify opportunities for marketplace growth and optimization Develop and refine metrics, dashboards, and KPIs to monitor market performance and guide strategic decisions Generate and validate hypotheses to address key market challenges, presenting insights and actionable recommendations to stakeholders Investigate anomalies and deviations in key metrics, identifying root causes and proposing solutions Build predictive models and advanced analytics frameworks to forecast market trends and inform resource allocation Skills, Knowledge and Expertise 4–7 years of industry experience in data analytics, data science, applied research, or PhD + 2–4 years post-PhD in industry End-to-end ownership of individual marketplace models, policies, or decision mechanisms Strong judgment in trading off model fidelity, latency, robustness, and business impact Proven ability to move from abstract problem formulation to production-grade decision logic Hands-on experience collaborating with engineering teams on deployment, iteration, and monitoring Demonstrated ability to reason about system behavior under uncertainty, delayed feedback, and partial observability Modeling supply-demand dynamics as stochastic, partially observed, feedback-driven systems; formalizing unit economics, service quality, and LTV as state variables and objectives Design and analysis of experiments under interference, spillovers, feedback loops, and adaptive policies (e.g., switchbacks, clustered interventions) Practical experience with DiD, panel methods, synthetic control, double-robust estimators, and causal ML approaches in production settings Ability to distinguish short-term experimental uplift from long-run equilibrium shifts and cumulative feedback effects using time-series and dynamic causal methods. Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries Develop your professional skills with access to mentoring, career consulting, and learning programs Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects Work alongside people who take initiative, speak openly, and challenge themselves to grow Improve your language skills through co-financed courses and internal speaking clubs Annual salary from EUR 70 700 gross Final benefits may vary depending on the location.

Prime Intellect is looking for an Applied Researcher to build and post-train agents for scientific research workflows.

Prime Intellect is looking for an Applied Researcher to build post-training and evaluation systems for multimodal computer-use agents.