Вакансии

Location: Ukraine; Poland; Spain; Portugal; Romania; Czechia At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed. We are looking for a Middle ML & AI Engineer to join us as part of our team. About the role PitchBook’s Machine Learning Engineer is responsible for using machine learning, statistical modeling, and natural language processing (NLP) to collect a high volume of data for the PitchBook Platform and surface insights for the capital markets. This role will also serve as a resource and mentor for junior members of the team. A strong motivation to succeed is critical, and everyone has the opportunity to shape the long-term direction of our team. About the project PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials, and many more. Team: Engineering Manager, 6 MLE’s. Work Environment: The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology. Technical Stack: Python, PyTorch, Scikit-Learn, Spacy, LangChain, LangGraph etc. As a qualified expert, You will Work with GenAI & LLMs: Design, develop, and fine-tune generative AI models using transformer architectures. Implement advanced techniques to create practical solutions for real-world challenges. Use Modern Toolkits: Leverage frameworks like LangChain, LangGraph, and LangSmith to build automated pipelines and intelligent agents. Integrate these tools with vector databases for efficient similarity search and real-time analysis. Extract Data Insights: Process and analyze large volumes of unstructured data using embeddings and statistical modeling to identify meaningful insights. Build Scalable Systems: Collaborate with cross-functional teams to create production-grade ML systems that handle real-time, high-volume data processing. Collaborate & Communicate: Work with Product and Engineering teams to improve data management and support innovation. Present complex analyses clearly and effectively. Tech Stack & Focus Areas: Work with generative AI and transformer models, integrating frameworks like LangChain, LangGraph, and LangSmith to automate workflows and build intelligent systems. Use PyTorch, Scikit-Learn, and Spacy to build NLP pipelines. Implement vector databases for high-dimensional data embeddings and develop hybrid models to extract insights from diverse data sources. We’re looking for you if you have Educational Background: You hold a Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field. Experience: You bring 4+ years of production experience in the ML field, with a track record in developing transformer-based models and deploying them in scalable systems. Technical Expertise: Proficient in Python and machine and deep learning frameworks like TensorFlow, PyTorch, ScikitLearn, Spacy, or others; experienced in natural language processing and modern LLM-based techniques; experience with full ML lifecycle, data preprocessing, training, evaluation stage, and monitoring techniques; skilled with SQL and data extraction methods. Innovative Thinker: You thrive on solving complex problems and are always exploring new trends in Generative AI. Collaborator & Communicator: You’re an excellent communicator able to collaborate and professionally express your thoughts to team members, other engineering teams, and stakeholders. Domain Knowledge: Exposure to financial services, investment banking, venture capital, or private equity is nice to have. Languages: English —Upper-Intermediate or higher; Ukrainian —fluent/native . What’s in it for You Reveal great tech solutions Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling clients growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients. Experience an agile and flexible working environment Work fully remote around the globe, or in a hybrid work model from our office hubs which are equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. Empower yourself with a stable workload and a stable income, focusing on lasting cooperation and high-performance culture. Embrace the opportunity for personal and professional growth Take advantage of performance review and career path. Elevate your skills with mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more. Feel cared about Be among like-minded people and feel supported with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. Interview steps Pre-Screening with the recruiter Technical interview (up to 90 min) System Design interview (up to 45 min) Final interview with Engineering Manager (60 min)

-Разработка и внедрение AI-агентов для автоматизации внутренних процессов (чат-боты, интеллектуальная обработка документов, автономные системы). -Проектирование, внедрение и оптимизация RAG-систем (поиск по базам знаний, корпоративным данным, векторные хранилища).

Разработка, развитие и обеспечение стабильности работы ИИ-сервисов; Интеграции со смежными внутренними системами компании по API;

Location: San Francisco, CA Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Sierra was co-founded by Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/ who first met while working together at Google over 20 years ago. Bret currently serves as Chairman of the Board of Open AI. Previously, he was co-CEO of Salesforce and CTO of Facebook. Clay spent 18 years at Google, where he most recently led Google Labs. Sierra’s Applied Research team works to make the models behind our agents better, faster, and cheaper across many kinds of tasks. Our work covers post-training open-weight models for specialized capabilities, new approaches to voice AI, and the benchmarks the field uses to measure agents, including τ-bench. As an Applied Research Intern, you’ll join the team for a research project, working with a mentor, you’ll pick a problem that matters to how AI agents perform in the real world, run rigorous experiments, and potentially write up what you find for a top venue. If you want your research to reach both the research community and production agents used by millions of people, come do it at Sierra. WHAT YOU’LL DO - Own a research project from start to finish, from framing the question and running experiments to writing and submitting a paper. - Push agent evaluation forward, for example by extending τ-bench to new domains, modalities, or long-horizon tasks, or by building new ways to measure reliability and consistency. - Study how agents reason, use tools, and follow policies over multi-turn conversations, and find out where and why they fail. - Explore post-training methods such as supervised fine-tuning and reinforcement learning to improve agent behavior, and measure the gains rigorously. - Work with large-scale, proprietary datasets of text and audio interactions to find research questions grounded in real customer problems. - Work with researchers and engineers across Sierra, and where it makes sense, turn your findings into improvements in production. WHAT YOU’LL BRING - Currently pursuing a PhD in computer science, machine learning, or a related field. - A research track record, such as first-author publications or submissions at venues like NeurIPS, ICML, ICLR, ACL, EMNLP, Interspeech or ICASSP. - Strong experimental skills: you design clean experiments, build careful baselines, and are honest about what the results do and do not show. - Fluency in Python and modern ML tooling (for example, PyTorch, and LLM inference and training stacks). - High agency and a builder’s mindset. You’ll work across the stack to get an experiment running. - Clear, direct communication, both in writing and when presenting research. EVEN BETTER - Research experience with LLM agents, tool use, or multi-turn dialogue. - Experience designing benchmarks or evaluations, especially ones tied to real-world outcomes. - Experience with supervised fine-tuning, reinforcement learning, or long-horizon optimization. - Experience with speech, voice, or multimodal models. - Open-source contributions to ML research code or benchmarks. INTERN QUALIFICATIONS - The internship is open to students currently enrolled in a PhD program. - Duration and start dates are flexible. Interns are embedded with the Applied Research team and work under the guidance of a mentor. - The internship is based in our San Francisco office. We are in person Monday through Thursday. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits - Lunch, as well as delicious snacks and coffee to keep you energized when working onsite - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region and employment type, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply through the link on this page even if your experience doesn't precisely match the job description. Sierra is a proud equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any characteristic protected by applicable federal, state or local laws, including veteran status and disability status. Hiring decisions are made by people, based on merit. If you need a reasonable accommodation for a disability, a religious practice, pregnancy or any related condition at any point in the application or interview process, contact us at accommodations@sierra.ai. Requests are handled confidentially and do not affect your candidacy. We will consider qualified applicants with arrest and conviction records in accordance with applicable fair chance laws.

Location: US - Headquarters - Maryland - Columbia Who is Tenable? Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500, 50 percent of the Global 2000, and large government agencies. Come be part of our journey! What makes Tenable such a great place to work? Ask a member of our team and they’ll answer, “Our people!” We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging, respect, and excellence where we can be our best selves. When you’re part of our #OneTenable team, you can expect to partner with some of the most talented and passionate people in the industry, and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together! Your Role: As an AI Security Engineer on Tenable's Information Security team, you will help shape and mature our approach to securing AI both within Tenable's products and across the enterprise. AI introduces a new class of risk at the intersection of application security, cloud risk, and data governance, and you will be on the front lines of defining how we assess, test, and address it. You will apply hands-on AI engineering experience alongside product security and application security skills to design security controls for AI systems, lead assessments, and help engineering teams ship AI features securely. You will establish security standards for responsible AI use, influence decisions across the organization, and build tooling that raises our security posture. This role is ideal for a practitioner who has spent time building or tinkering with AI systems, looking under the hood of model internals, and wants to channel that experience into a security specialty. Your Opportunity: Design, implement, and maintain end-to-end security controls across the AI/ML lifecycle. Assist in the implementation of robust security guardrails, controls, and boundary mechanisms for Large Language Models (LLMs), Small Language Models (SLMs), and open-source models. Continuously monitor the AI landscape for novel vulnerabilities and attack techniques, applying deep engineering experience to proactively defend enterprise AI deployments. Assist in developing security reference architectures for AI deployment patterns, including MCP servers and agentic AI workflows and harnesses. Deploy controls to ensure model integrity, governance, and proper access control across models and feature stores. Build AI-driven tooling to strengthen cybersecurity posture across identity, incident management, vulnerability management, third-party risk, and emerging AI threat vectors. Perform AI safety, security assessments, threat modeling for AI/ML systems, identifying risks such as data poisoning, model evasion, model extraction, adversarial inputs, and integrity attacks. Collaborate with software engineers, and product teams to integrate security best practices into AI development, training, validation, and deployment processes. Create security guidance, documentation, and training for internal engineering and development teams; develop metrics that drive desired security behaviors and outcomes. Assist in researching emerging trends in AI security and contribute to innovative solutions that support Tenable's AI initiatives. What You'll Need: 2 or more years of professional experience in information security or application security, with at least a demonstrated interest on securing AI/ML systems. Bachelor’s Degree in Computer Science, Cybersecurity, Data Science, or a related field preferred. Awareness of AI-specific security threats, including adversarial ML, data poisoning, prompt injection, model inversion, model evasion, and inference attacks. Knowledge of data security principles, including encryption, masking, and tokenization. Knowledge of SSDLC, DevSecOps, and security testing practices, including SAST, DAST, SCA, and threat modeling. Knowledge of application security architecture best practices and patterns, including modern web applications, Docker, and microservices. Ability to work cross-functionally across engineering, product, and business teams. Strong written and verbal communication skills; able to clearly translate complex AI security risks for both technical and non-technical audiences. Strong problem-solving skills, attention to detail, and ability to lead projects with end-to-end ownership. Self-motivated and effective working independently and across distributed teams. And Ideally: Hands-on experience with AI red teaming, adversarial prompt testing, or LLM security assessments. Basic coding experience in Python and/or Go. Experience with frameworks & libraries such as PyTorch, Hugging Face Transformers, TensorFlow or similar. Strong understanding of the ML/AI lifecycle and the security risks associated with each stage. Experience with application security assessment tools (Burp Suite, ZAP, Tenable WAS, etc.) Knowledge of cloud security platforms: AWS, Azure, or GCP including AI/ML-related services. Security certifications: CISSP, CSSLP, SANS GIAC, CEH, or AI-focused security certifications are a plus, but not necessary. #LI-Hybrid #LI-MS1 This is the base pay range for this position. Compensation for the role will depend on a number of factors, including the candidate's qualifications, skills, competencies, location and experience, and may fall outside of the range shown. Employees are also eligible for variable compensation in addition to base pay (commission for sales roles, bonus for non-sales roles), depending on company and individual performance. Tenable also offers a variety of comprehensive and competitive benefits which include: medical, dental, vision, disability and life insurance; 401(k) retirement savings with company match; an employee stock purchase plan; an employee referral program; flexible spending accounts; an Employee Assistance Program (EAP); education assistance; parental leave; paid time off (PTO); company-paid holidays; health and wellness events; and community programs. US Pay Range $102,500 — $136,500 USD We’re committed to promoting Equal Employment Opportunity (EEO) at Tenable - through all equal employment opportunity laws and regulations at the international, federal, state and local levels.  If you need a reasonable accommodation due to a disability during the application or recruiting process, please contact Recruiting@Tenable.com for further assistance. Tenable Data Consent Statement Tenable is committed to protecting the privacy and security of your personal data. Depending on your location, one or more of the following notices may apply to you: General Applicant Privacy Notice California Applicant Privacy Notice EU/EEA/UK Applicant Privacy Notice

Location: Москва Workplace: Гибридный О компании Мы находимся в поисках Технического руководителя по исследованию данных (Data Science). Вы будете развивать команду Больших данных и ИИ в продукте CVM B2B. Обязанности Управлять командой до 15 человек; Реализовывать функционал RoadMap продукта по поставке данных, разработке/развитию ML-моделей в рамках контекста B2B продаж и клиентского сервиса; Заниматься формированием аналитического профиля B2B и подложек для ML-моделей для скоринга продуктов и услуг B2B экосистемы МТС. Требования Опыт работы в роли Data Science от 5 лет; Опыт управления командой от 3 лет; Опыт построения ML-моделей; Опыт работы со стеком: Python, SQL, Pandas, NumPy, scikit-learn, PyTorch Hadoop, Hive, NiFi, GreenPlum, ClickHouse, Apache Spark, Apache Airflow. Условия собственную платформу MTS Ocean для получения ИТ-ресурсов, а это значит, что деплой, мониторинг, observability — не будут для тебя проблемой, ты сможешь сосредоточиться на фичах; профессиональные гильдии инженеров, где мы поддерживаем друг друга и помогаем стать лучше; внутреннюю площадку TechTalks для обмена опытом, дискуссий, развития навыков самопрезентации; участие во внешних IT конференциях. Наши эксперты выступают на ведущих профильных конференциях, таких как HighLoad++, DevOps Conf, Joker, AHA!, Матемаркетинг и другие. А еще мы организуем собственную масштабную конференцию True Tech Day и регулярно проводим митапы для разработчиков и инженеров. полезные курсы и вебинары в корпоративном университете и электронную библиотеку; ДМС с первого месяца работы, включая стоматологию; страхование от несчастных случаев с 1 месяца работы. Материальную помощь в сложных жизненных ситуациях; отпуск 28 календарных дней (+3 дополнительных дня за ненормированный рабочий день); прием врачей общей практики и массаж в офисе; мобильная связь за счет компании и льготные тарифы для близких; подписка на онлайн-кинотеатр KION, сервис МТС Музыка, книжный сервис Строки от МТС, безлимитные мессенджеры и соцсети.

- Развивать направление Data Science в маркетинге. - Строить и улучшать предиктивные модели вероятности платежа.

– Уверенно владеешь Python и понимаешь связанные с ним фреймворки: tf, pytorch, numpy, librosa, opencv.

Предстоит исследовать LLM и RAG, работать с неструктурированными данными, готовить датасеты и обучать chat- и embedding-модели. Среди задач также AI-инструменты и агенты для корпоративных продуктов.

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: Paris; London Workplace: Hybrid 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. ABOUT THE JOB Mistral AI is seeking Applied Scientists and Research Engineers focused on time series. You will design, train, and deploy state-of-the-art time-series foundation models, and apply them to the most demanding forecasting problems in industry, from power grids and plants to trading desks. Timestral, our family of time-series foundation models, ranked first on amongst all benchmarks at release (tech report https://arxiv.org/pdf/2610.37585v1: https://arxiv.org/pdf/2610.37585v1 ). The role has two sides: building the next generations of Timestral with our Science team, and taking them into production with our industrial and financial clients, adapted to their data. Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions. We are made of researchers, engineers, product builders, ex-founders who love tackling the most complex real world problems in the industry. WHAT YOU WILL DO - Run pre-training, post-training and deploy state of the art time-series foundation models on clusters with thousands of GPUs. - Generate and curate large-scale time-series datasets, real and synthetic, and build evaluators and benchmarks for forecasting and other time-series tasks. - Push the frontier of Timestral: architectures, training recipes, scaling, and new capabilities such as anomaly detection, classification and embeddings. - Fine-tune and adapt Timestral on client data, in the cloud or on-premise, and benchmark it against the client's existing models. - Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment. - Collaborate with cross-functional teams to tackle complex use cases, including agents that combine Timestral with Mistral LLMs. - Manage research projects and communications with client research teams. ABOUT YOU - Fluent in English with excellent communication skills, at ease explaining complex technical concepts to both technical and non-technical audiences. - Expert with PyTorch or JAX. - Solid knowledge of modern deep learning and large language models: architectures, pre-training, post-training and evaluation. - Experience pre-training or fine-tuning time-series foundation models or large transformer models at scale. - Strong background in time-series modelling: probabilistic forecasting, multivariate series and covariates, and classical baselines (statistical models, gradient boosting). - Not afraid of contributing to a big codebase; finds their way around independently with little guidance. - Writes clean, readable, high-performance, fault-tolerant Python code. - Doesn't need roadmaps: moves independently and is comfortable with uncertainty. - Low-ego, collaborative and eager to learn. - Track record of success through personal projects, professional projects or academia. NICE TO HAVE - Ex-founder or builder - PhD / Master in a relevant field (Machine Learning, Statistics, Applied Mathematics, Physics). Exceptional candidates from other backgrounds welcome. - Research on time-series foundation models: pre-training, scaling, synthetic data or evaluation. - Hands-on experience training or fine-tuning LLMs, building agents and tool use, or models that combine text and time series. - Industry experience in energy, industrial operations, telecom or finance, or experience deploying models for industrial clients. - Experience working on large code bases or large machine learning models at scale. - Contributions to a large open-source or industry codebase. - Publications in top academic journals or conferences. - Enjoys improving existing code: typing, tests, CI pipelines. 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: 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.

We are looking for a Senior Machine Learning Engineer to develop, deploy, and maintain machine learning and AI solutions for advanced financial data processing and reconciliation platforms. You will work with large and complex financial transaction datasets to build solutions for transaction matching, reconciliation, and exception handling.

Проектировать, разрабатывать и оптимизировать ML-пайплайны: от подготовки данных и обучения моделей до внедрения в production. Разрабатывать и дообучать ML/NLP-модели под продуктовые задачи.

Location: Москва Мы новое подразделение Сбера, занимающееся созданием инструментов и решений для внутренней безопасности с помощью AI. Наши задачи направлены на развитие AI-агентов, создание RAG, запуск локальных инстансов LLM и их файнтюнинг. Наши планы имеют широкий спектр - начиная от разработки DL-моделей для скоринга, поведенческого моделирования и заканчивая графовыми нейронками для выявления скрытых связей. Сейчас мы в поисках Middle Data Scientist, который будет разрабатывать AI-агентов, адаптировать LLM под специфику задач, строить RAG-системы. Если ты хочешь иметь возможность погрузиться в глубины LLM и Deep Learning, пробовать новые методы к файнтюнингу LLM, строить AI-агентов, то мы ждем тебя в нашу команду. Обязанности * cоздание и развитие решений с задействованием LLM-агентов (полноценные мультиагентские системы) * cоздание RAG-систем * разработка и поддержка ML/DL моделей, включая их обновление и адаптация к меняющимся условиям и требованиям бизнеса * дообучение и адаптация открытых LLM * создание системы мониторинга качества моделей и контроль их эффективности. Требования * опыт работы от 3-х лет в коммерческой разработке на позиции Data Scientist / ML Engineer с подтверждённым опытом вывода моделей в продакшен * понимание математики классического ML и архитектуры нейросетей (PyTorch/TF) * уверенное понимание архитектуры трансформеров, опыт работы с LLM API и локальными моделями через PyTorch / Hugging Face * уверенный Python (чистый код, ООП, знание numpy/pandas/scikit-learn) и понимание принципов микросервесной архитектуры (Asyncio, FastAPI) * опыт создания агентов на LangChain/LangGraph * знание основных концепций промпт-инжиниринга и использования Structured Outputs и Function Calling * опыт проектирования и развития RAG-систем (Multi-stage Retrieval, Dense + Sparse/BM25, Rerankers, Chunking strategies) * опыт развертывания локальных open-source моделей (Llama, Mistral, Qwen, DeepSeek и др.) в продакшн-окружении и настройки высокопроизводительного инференса LLM с помощью vLLM * опыт проведения Fine-tuning LLM-моделей (PEFT, LoRA/QLoRA) * настройка мониторинга data drift и model decay, а также валидация моделей * навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. Условия * комфортный современный офис рядом с м. Кутузовская * офисный формат работы * 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 (для Junior позиций) * 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: Brussels, Belgium We currently have a vacancy for a Data Scientist fluent in English, to offer his/her services as an expert remotely (within EU) . In the context of the first assignment, the successful candidate will be integrated in the Development team of the company that will closely cooperate with a major client’s IT team. Tasks: Social media data scraping and analysis to study platform features impact on users; Evaluating the effectiveness of different recommender systems and algorithms on social media platforms; Designing and implementing experiments with sock puppets methodologies (account-based data collection) to test the impact of recommender systems on users; Designing and implementing data visualisations to communicate social media insights to stakeholders; Provide comprehensive project support throughout its lifecycle, including the development of work plans and timelines, meetings and stakeholder interactions, tracking of infrastructure requirements and resource allocation, monitoring of tasks progress against milestones and deliverables, and ensuring effective communication between technical teams and project leadership. Requirements University degree in IT or relevant discipline, combined with minimum 7 years of relevant working experience; Minimum 3 years of professional experience in data science, data analysis, data visualisation and quantitative research, including designing and implementing data pipelines, data cleaning and data processing; Minimum 3 years of experience in statistical analysis, machine and deep learning model development, and model evaluation; Minimum 2 years of experience in account-based data collection, scraping, and working with large-scale datasets from digital or social media sources; Knowledge of programming languages (e.g., Python, R, SQL; Perl optional) commonly used in data science for modelling and automation; Knowledge of machine learning, deep learning and natural language processing (e.g., Scikit-learn, TensorFlow, PyTorch, Hugging Face); Knowledge of web scraping tools and libraries, large scale data collection from social media platforms, including account-based methodologies (e.g., sock puppets); Knowledge of data engineering and data visualization tools; Knowledge of agile development methodologies and version control systems such as Git; Knowledge of project management software such as JIRA and Microsoft Project; Knowledge of collaboration and documentation platforms such as Confluence and SharePoint; Certifications in Computer Science, Engineering, Statistics, Mathematics, or related field; Excellent command of the English language. Benefits If you are seeking a career in an exciting and dynamic company, where you will offer your services as part of a team of a major European Institution, operating in an international, multilingual and multicultural environment where you can expect real chances to make a difference, please send us your detailed CV in English, quoting reference: (21652/11/2026). We offer a competitive remuneration (either on contract basis or remuneration with full benefits package), based on qualifications and experience. All applications will be treated as confidential. You may also consider all our other open vacancies by visiting the career section of our web site ( www.eurodyn.com) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS ( www.eurodyn.com ) is a leading Software, Information and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Stockholm, London, Nicosia, Hong-Kong, Valetta, etc). The company employs over 1000 engineers, IT experts and consultants (around 3% PhD, 36% MSc and 53% BSc or equivalent). We design and develop software applications using state-of-the-art technology. The group generates annual revenues in the range of EURO 40 million, with an EBITDA in the range of 20%. The value of our contract portfolio exceeds EURO 300 million. EUROPEAN DYNAMICS is a renowned supplier of IT services to government institutions, multinational corporations, public administrations and multinational companies, research and academic institutes. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, color, national origin, ethnicity, gender, disability, sexual orientation, gender identity, or religion EUROPEAN DYNAMICS (ED) adheres to the General Data Protection Regulation principles by applying its Privacy Policy as published in www.eurodyn.com/privacy. By submitting an application to this position and by sharing your personal data with ED, you acknowledge and accept its Policy and authorize ED to process your personal data for the purposes of the company's recruitment opportunities, in line to the Policy.

Location: Remote - United States Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. We are seeking an experienced Technical Recruiter to support high-volume and specialized hiring efforts for technical roles across our organization. The ideal candidate has a proven track record of successfully recruiting Data Scientists, Machine Learning Engineers, AI Engineers, and other highly technical talent in a fast-paced environment. This is a full-time position with the opportunity to make an immediate impact by building strong candidate pipelines and partnering closely with hiring managers to fill critical technical roles. Key Responsibilities Manage the full-cycle recruitment process from intake through offer acceptance. Partner with hiring managers to understand technical hiring needs, required skills, and team objectives. Source, identify, and engage passive candidates using LinkedIn Recruiter, job boards, networking, and other sourcing tools. Recruit for highly specialized technical positions, including: Data Scientists Machine Learning Engineers AI Engineers MLOps Engineers Data Engineers Software Engineers (Python, Cloud, AI) Screen resumes and conduct initial candidate interviews to evaluate technical qualifications, communication skills, and cultural fit. Coordinate interview schedules and maintain consistent communication with candidates and hiring managers throughout the recruitment process. Build and maintain talent pipelines for future hiring needs. Ensure an exceptional candidate experience from first contact through onboarding. Track recruiting metrics and maintain accurate records in the Applicant Tracking System (ATS). Stay current on market trends, compensation, and emerging technologies within AI and machine learning. Required Qualifications 5+ years of full-cycle technical recruiting experience. Demonstrated success recruiting Data Scientists, Machine Learning Engineers, AI Engineers, and other advanced technical professionals. Strong understanding of technical terminology, AI/ML concepts, cloud technologies, and software engineering roles. Experience sourcing passive candidates through LinkedIn Recruiter and other recruiting platforms. Excellent interviewing, communication, and relationship-building skills. Experience working with Applicant Tracking Systems (ATS). Ability to manage multiple requisitions simultaneously while meeting hiring deadlines. Preferred Qualifications Experience recruiting for startup or high-growth technology companies. Familiarity with cloud platforms (AWS, Azure, or Google Cloud). Experience hiring professionals with expertise in Python, SQL, TensorFlow, PyTorch, Spark, Kubernetes, or similar technologies. Experience recruiting for remote and distributed engineering teams. The expected salary range for this position is $140,000 – $160,000 USD per year, based on experience, skills, and qualifications. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Location: Москва В Центре Робототехники Сбера мы строим антропоморфного робота общего назначения. Наш робот вышел из стадии первых демонстраций — и мы переходим к надёжной автономной работе в реальных задачах. Локомоция и управление всем телом (Whole Body Control) — основа этой работы: именно от них зависит, насколько устойчиво и предсказуемо робот движется в реальном мире. Мы ищем инженера, который разрабатывает, обучает и доводит до робота RL-политики управления всем телом — от постановки задачи в симуляторе до стабильной работы контроллера на железе. Это роль для того, кто хочет видеть результат своих алгоритмов не в метриках на графике, а в том, как робот уверенно идёт, поворачивает и держит равновесие. Наша команда действительно работает над уникальным проектом в России, а у Вас будет прямой доступ к железу, Вы будете видеть результат своей работы в физическом мире. **Ключевые технологии:** Python, PyTorch, RL (PPO/SAC), MuJoCo/Isaac Sim, ONNX, C++, ROS2. Обязанности * разрабатывать, обучать и деплоить RL-политики локомоции и whole body control * подбирать observations, actions и типы моделей, которые дают максимальную производительность * находить и закрывать ключевые факторы в sim-to-real gap — добиваться, чтобы политика, обученная в симуляторе, работала на реальном роботе * определять, измерять и интерпретировать метрики качества обученных политик * доводить control stack до стабильной и предсказуемой работы в реальных условиях * настраивать reward-функции, domain randomization и curriculum под конкретные двигательные задачи * работать в связке с командами интеграции и middleware — доводить политику до надежного исполнения на железе. Требования * уверенное владение Python; знание C++ как плюс для интеграции на робота * опыт применения RL-алгоритмов для робототехники или управления (PPO, SAC и подобные) * понимание динамики и управления, в идеале — шагающих роботов * опыт подбора гиперпараметров и cost/reward-функций для RL * знакомство с типовыми техниками RL: domain randomization, curriculum learning, reward shaping * опыт работы с симуляторами (MuJoCo, Isaac Sim или аналоги) * способность самостоятельно разбираться в незнакомых системах и находить корневые причины проблем * желание брать ответственность за то, что политика работает на роботе, а не только в симуляторе. **Будет плюсом:** * опыт деплоя обученных политик на реальное железо * опыт с behavior cloning и дистилляцией политик * понимание основ теории управления, кинематики и динамики шагающих роботов * опыт работы с middleware робота (ROS2, DDS) * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия * комфортный современный офис г. Москва, МЦК ЗИЛ, Автозаводская 23а к2 * **формат работы - офис** * 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 (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.