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Location: Remote - India Who we are At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences. Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands. . Hiring and how we work We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions! Also, while we are a remote-first company, you may be asked to report in person on an ad-hoc basis for team gatherings, functional off-sites or customer meetings. . See yourself at Twilio Join the team as our next Principal Machine Learning Engineer in Twilio’s Customer Memory Team. About the job In this role, you will build the foundational AI/ML capabilities that power intelligent engagement experiences for Twilio customers - letting any application, human or AI agent, or channel know who a customer is, what they have done, and how best to reach them. This role requires a builder and curious mindset, someone who can bridge the gap across theoretical research, production-grade experimentation and enterprise quality software. You will lead rapid research cycles to solve complex problems regarding contextual intelligence and large scale ML problems ensuring every interaction feels seamless and personalized. Responsibilities In this role, you’ll: Build out new functionality within a fast moving product seeing rapid adoption. Take a nebulous idea, design a rigorous experiment, and execute it within weeks. Conduct deep-dive research into state-of-the-art LLM orchestration, retrieval strategies, and data-driven personalization. Build ML systems that hold up at Twilio scale. Rapidly acquire new technical skills and knowledge in a fast-paced, high-delivery environment. Proactively leverage and integrate the latest AI development stacks to bypass boilerplate engineering and focus on core differentiation. Qualifications Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table! *Required: 10+ years applied ML/AI experience with a track record of owning technically ambitious systems end to end rather than contributing to someone else's design. Comfort operating in a 0-to-1 setting with ambiguous requirements and a strong bias for action. Proficiency in Python programming language. Experience building cloud-based services on AWS, GCP, or Azure, with high-volume data, streaming or real-time inference paths, and a range of data stores. Deep expertise in the design, architecture, and deployment of production-grade ML/AI systems, including LLM orchestration, embedding models, and vector stores. Strong fundamentals of statistical machine learning algorithms, transformer models, LLMs Ability to raise the technical bar of a team through mentorship, design review, and standards, without formal management authority. Experience in building cloud-based services using AWS, GCP, or Azure. Strong communication skills and highly effective collaborator who works well with teammates. Desired: Prior experience building autonomous agents that handle multi-turn conversations and maintain long-term context/consistency. Strong background in predictive modeling on user behavior, including experiment design and the causal or counterfactual methods needed when randomization is partial. Prior experience working in a globally distributed team. Location This role will be remote, and based in India - Karnataka, Tamil Nadu, Telangana State, Maharashtra and New Delhi. Travel We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings. What We Offer Working at Twilio offers many benefits, including competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more. Offerings vary by location. Twilio thinks big. Do you? We like to solve problems, take initiative, pitch in when needed, and are always up for trying new things. That's why we seek out colleagues who embody our values — something we call Twilio Magic . Additionally, we empower employees to build positive change in their communities by supporting their volunteering and donation efforts. So, if you're ready to unleash your full potential, do your best work, and be the best version of yourself, apply now! If this role isn't what you're looking for, please consider other open positions. . Stay alert to recruitment fraud We care about your safety. Scammers sometimes impersonate Twilio recruiters through fake job postings, emails, websites, or messages. Please ensure you are engaging with an official @ twilio.com email address. We will never ask for payment, gift cards, cryptocurrency, or banking information during the recruiting process. We do not make job offers without a formal interview process or conduct interviews exclusively through text-based messaging apps. . Twilio is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Additionally, Twilio participates in the E-Verify program in certain locations, as required by law.

Location: Paris 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 Evaluation is how we decide which models, checkpoints and recipes ship. As a Research Engineer on the Eval Platform team, you will build the infrastructure every science team relies on to measure model quality, and make it reliable, reproducible and fast. You don't need to have designed benchmarks before. You do need to care about what a score means, and about when a difference between two runs is real. WHAT YOU WILL DO - Build systems that keep eval results reproducible and comparable over time, as models, benchmarks and code evolve. - Run evaluations at scale across our GPU clusters, from model serving to scoring. - Make eval results easy to access, explore and trust, through APIs and dashboards that researchers use every day. - Catch broken or noisy evals before they mislead research decisions. - Support evaluation of agentic, multi-turn and tool-using models. - Work closely with researchers to turn new evaluation needs into robust, shared tooling. WHAT WE'RE LOOKING FOR - Master's or PhD in Computer Science, or equivalent experience. - 4+ years building production-grade software, ideally large-scale ML codebases or distributed systems. - Excellent Python and strong software-design instincts: testing, code review, CI/CD. - Experience running workloads on GPU clusters (Slurm, Kubernetes, Ray or similar). - Familiarity with LLM inference and evaluation. - A product mindset: researchers are your users. - Self-starter, low-ego, collaborative. NICE TO HAVE - Experience building or maintaining evaluation harnesses or benchmarks. - Hands-on experience with inference engines such as vLLM or SGLang. - Experience with agentic or RL environments. - Statistics for experimentation: variance estimation, significance testing. - Open-source contributions to ML tooling. 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: Limassol, Cyprus; Warsaw, Poland; Tallinn, Estonia Workplace: none David Kennedy Recruitment is working with a global, fast-growing company that is seeking to onboard a Senior AI/ML Engineer – Applied AI Lead to join their team. The role focuses on taking machine learning solutions from development through to production, establishing robust MLOps practices and delivering AI use cases with measurable business impact. Position: Senior AI/ML Engineer – Applied AI Lead Location: Tallin/Limassol/Warsaw Work model: On-site in Tallin/Limassol, Hybrid in Warsaw Employment type: Full-time DUTIES AND RESPONSIBILITIES: Design, develop and productionise machine learning models across training, validation, deployment, monitoring and retraining Lead AI use cases including client lifetime value, churn prediction and fraud or abuse detection Build and establish robust MLOps practices, including deployment pipelines, CI/CD, environment promotion and model lifecycle management Implement model monitoring frameworks covering performance, data drift, data quality and business impact Establish retraining and escalation strategies for production models Implement model explainability and transparency using SHAP, feature attribution and other appropriate interpretability techniques Define and enforce best practices around model governance, documentation, versioning and auditability Collaborate with Data Engineering on data pipelines, feature engineering, reproducibility and scalable data foundations Partner with Product, Risk, Commercial and other stakeholders to translate business problems into pragmatic AI solutions Drive continuous improvement through monitoring insights, feedback loops and model retraining Mentor team members and promote best practices in production AI, MLOps and applied machine learning delivery REQUIREMENTS: 5–8+ years of experience building and deploying machine learning models in production environments Strong Python programming skills and solid software engineering fundamentals Strong understanding of machine learning concepts, model evaluation and feature engineering Practical understanding of production ML considerations including data leakage, drift and model stability Hands-on experience with Spark / PySpark and large-scale data processing Experience with MLflow or similar ML lifecycle tools Experience building and maintaining CI/CD pipelines , preferably with GitHub Actions Strong SQL skills and experience working with large, complex datasets Proven ability to deliver AI/ML solutions with measurable business impact Experience with model deployment, monitoring, drift detection and retraining strategies Strong communication skills with both technical and non-technical stakeholders Ability to balance MVP delivery speed with production robustness in evolving environments OFFER: Attractive remuneration package based on qualifications and experience Employee Training & Development programme Multiple team and group events Birthday and loyalty benefits And much more!

Задачи: — Вести разработку и проектирование систем компьютерного зрения для биометрии (атрибуты лица, детекция, качество и распознавание). — Проводить тщательный анализ справедливости и бенчмаркинг биометрических моделей на различных наборах данных и условиях эксплуатации. — Архитектурировать, обучать и оптимизировать модели с использованием PyTorch, Tensorflow и/или JAX. — Владеть и развивать сквозные ML-пайплайны от сбора данных до деплоя. — Проектировать автоматизированные пайплайны (Airflow) для сбора и очистки данных. — Курировать сбалансированные обучающие наборы и генерировать синтетические данные для устранения проблем с качеством и разнообразием. — Обеспечивать производство: отвечать за путь модели до продакшена. — Оптимизировать модели для низкой задержки вывода (квантование, дистилляция, TensorRT/ONNX) и управлять деплоем на AWS. — Наставлять ML-инженеров, проводить код- и дизайн-ревью, продвигать технические лучшие практики в команде компьютерного зрения. Требования: — Опыт работы в индустрии машинного обучения не менее 5 лет, из них минимум 3 года в биометрии или анализе лиц. — Глубокие знания в области компьютерного зрения и биометрии, особенно в распознавании лиц. — Понимание источников алгоритмического смещения в компьютерном зрении и практический опыт измерения и смягчения разного воздействия. — Экспертное владение Python и библиотеками для машинного обучения и компьютерного зрения (Pillow, OpenCV, PyTorch и др.). — Умение писать чистый, модульный, готовый к продакшену код. — Опыт проектирования сквозных ML-пайплайнов и работы с оркестраторами рабочих процессов, такими как Airflow. — Практический опыт масштабирования обучающих задач на мульти-GPU кластерах и деплоя сервисов на AWS (SageMaker, EC2, EKS). Желательно: — Публикации в CVPR, ICCV, ECCV или FG по темам распознавания лиц, оценки качества изображений или справедливости. — Опыт работы с векторными базами данных (Milvus, Faiss) и алгоритмами приближенного поиска ближайших соседей. — Знания в области приватности, безопасности и соответствия в биометрических системах. — Опыт портирования моделей на edge-устройства или мобильные платформы с использованием CoreML, LiteRT, TFLite. — Опыт генерации синтетических лиц с помощью GAN или диффузионных моделей. — Сильные коммуникативные навыки. Условия: — Полная занятость. — Удалённая работа из Индии. О компании: Jumio — B2B технологическая компания, специализирующаяся на борьбе с онлайн-мошенничеством, отмыванием денег и финансовыми преступлениями с помощью AI, биометрии, машинного обучения и автоматизации. Компания является лидером в области онлайн-верификации личности, eKYC и AML решений, работает с клиентами по всему миру в различных отраслях. Политика конфиденциальности: Личные данные кандидатов используются только в рамках процессов подачи заявки, рекрутинга и найма в Jumio, согласно их Applicant Privacy Notice.

Location: San Francisco Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE This role sits at the applied end of our post-training research efforts. You will work directly with stakeholders from the world’s fastest-growing AI companies to post-train open-source models that outperform frontier closed models on their specialised tasks. Your day-to-day is finding creative ways to extract signal from complex, domain-specific datasets and building the reward functions, environments, eval harnesses, and training pipelines that turn that signal into better models. The models you train ship to production and reach millions of users. We are looking for people with hands-on LLM fine-tuning and RL experience. Researchers who are excited by the prospect of shipping models into production, who can translate a customer's domain-specific requirements into an effective training curriculum, and who know when to be rigorous and when to iterate fast. RECENT RESEARCH - Dense, on-policy or both? https://www.baseten.co/research/dense-on-policy-or-both/ - Repeated kv cache for long-running agents https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/ - Distillation without the dark – replicating black-box on-policy distillation on Baseten https://www.baseten.co/research/distillation-without-the-dark/ RESPONSIBILITIES - Design and run post-training pipelines: SFT, GRPO, DPO, RLVR, reward function engineering, and synthetic data generation. - Build task-specific training environments and evals tailored to customer domains like healthcare, code generation, and legal, spanning multi-turn tool use, sandboxed execution, and agentic workflows. - Work directly with customers to translate production data into training signal, designing reward loops from real usage patterns and handling distribution shift. - Run and analyze training experiments end-to-end: diagnose reward hacking, importance sampling drift, and advantage estimation instabilities. - Publish findings at top venues and contribute to Baseten's open-source training libraries. QUALIFICATIONS - Hands-on experience training LLMs with reinforcement learning — demonstrated understanding of GRPO or PPO beyond recipe-level reproduction, including group advantage computation, clipped objectives, and KL penalty design - Strong intuition for reward engineering: the ability to distinguish between a reward that trains effectively and one that will exploit at scale - Experience building multi-turn agent environments with tool use, not limited to single-turn question-answering setups - Comfort working across the full pipeline from dataset construction through training, evaluation, and deployment - Experience with production ML systems. Preference for candidates who have closed a training–inference loop where production data feeds back into model improvement PREFERRED QUALIFICATIONS - Experience with RL training frameworks - Publications at NeurIPS, ICML, ICLR, focused on RL for LLMs, reward modeling, or alignment BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Задачи: — Разрабатывать человекоподобных чат-ботов, которые не только ведут естественный диалог, но и инициируют и выполняют пользовательские сценарии внутри продукта — Участвовать в архитектуре и разработке интеллектуальной системы перевода, сохраняющей смысл, тон и стиль — Проводить эксперименты и внедрять ML/NLP-функции в продакшен с высокой стабильностью и измеримыми результатами — Работать с командами продукта и инженерии для обеспечения быстрой, точной и надежной интеграции ML-компонентов с бэкенд-сервисами — Отвечать за полный жизненный цикл ML-функций: сбор данных, эксперименты, обучение моделей, деплой, мониторинг и улучшение качества Требования: — Опыт разработки на Python и поддержки production-сервисов от 3 лет — Опыт веб/API разработки: REST, версионирование, логирование, наблюдаемость — Уверенные навыки Pandas, NumPy и SQL — Опыт решения реальных ML-задач (NLP/CV) в продакшен-проектах от 1 года — Опыт с TensorFlow, Keras и/или Hugging Face — Понимание практик деплоя и мониторинга моделей — Знание GIT, HTTP(s), JSON/XML и базовых сетевых протоколов (TCP/IP) — Понимание основ SDLC, code review и CI/CD пайплайнов — Знакомство с архитектурами на базе LLM: prompt engineering, RAG/fine-tuning, интеграция с бэкендом — Опыт создания интеллектуальных чат-ботов, выполняющих реальные действия в продукте, а не только FAQ или поддерживающих ботов Плюсы: — PyTorch, SciPy, OpenCV, Matplotlib, LangChain или LlamaIndex — Docker, NoSQL или векторные базы данных (Weaviate, PGVector, Milvus) — Базовые знания JavaScript для интеграции ML-сервисов с фронтендом — Генерация изображений на основе диффузии (Flux, LoRA, оптимизация инференса) — Автоматизированные фреймворки оценки LLM, включая офлайн/онлайн тестирование и A/B тесты — Опыт работы с мультимодальными моделями (текст↔изображение, vision-language) Условия: — Удалённая работа — Зарплата от $10,000 gross — 2 недели оплачиваемого отпуска в год — Оплачиваемые больничные — Профессиональные курсы за счёт компании — График работы: воскресенье–четверг, 10:00–19:00 по израильскому времени

Location: United Kingdom; Poland; Portugal; France; Spain; Germany; Italy; The Netherlands Workplace: Remote Recognised on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads. The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla. We’re on a mission to transform how companies use data. Come be a part of our journey! ABOUT THE TEAM The AI/ML Engineering team builds and operates ClickHouse's AI and machine learning products end-to-end. This includes the Agentic Data Stack, AI Functions, chDB, the in-Console copilot, and the AI/ML partnerships that distribute them — together with the shared components and expertise that let every other ClickHouse team ship AI in the surfaces they own. Our team is looking for highly skilled and experienced software engineers to join us, who will be responsible for designing, building, and operating the products that make ClickHouse the platform of choice for agents and data scientists.   WHAT YOU WILL DO - Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform. - API Architecture: Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services. - Ecosystem Integrations: Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools. - Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation. - Oncall: Participate in the daytime oncall rotation along with the rest of the team. - ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products. WHAT YOU WILL BRING ALONG - 5+ years of software engineering experience in production environments - Exposure to working directly with AI/ML technologies - Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases - A high level of ownership and can drive features from concept to production with minimal supervision - Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders - Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions Nice to have - Familiarity with cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment - Understanding of database systems and data processing pipelines, with ClickHouse experience being a significant plus - Experience with embedding-based systems (retrieval, ranking, recommenders) and the representation-learning ideas underneath them PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Location: London, England Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do. Our Global Engineering teams tackle complex technical challenges across a global, three-sided marketplace, building and scaling systems that serve millions of customers, riders and partners every day. We're looking for a Senior Machine Learning Engineer to join our London office as part of a Global DoorDash Engineering team (working hybrid, 3 days in the office). This role sits in our Consumer Org, where you'll work on recommendation problems with direct, measurable impact on how customers discover relevant content. You’ll collaborate with experienced scientists and engineers across DoorDash, Deliveroo and Wolt, learning from multiple recommendation systems while helping shape the next generation of the experience. What You'll Be Doing Design, develop and improve recommendation, ranking and retrieval models that surface relevant restaurants, dishes, items and content to customers. Own applied ML problems end to end: frame the problem, analyze data, develop models, define offline evaluation, run experiments and monitor production performance. Develop methods that balance relevance with product and customer needs, such as diversity, availability, business constraints and changing user intent. Collaborate closely with Software Engineers, ML Engineers, Product Managers and Analysts to turn scientific insights into reliable customer-facing products. Evaluate and apply state-of-the-art ML methods where they meaningfully improve recommendation quality, robustness or efficiency. Contribute to a high bar for applied-science practice through technical reviews, knowledge sharing and thoughtful experimentation. What You'll Need to Thrive You have substantial hands-on experience applying machine learning to real-world problems and a track record of bringing models from development into production; a PhD with relevant applied research experience is equally welcome. You have experience with recommendation systems, ranking, retrieval, personalisation, or closely related ML problems. You can independently turn an ambiguous customer or product problem into a well-scoped ML approach, make sound trade-offs and drive it to a measurable outcome. You are proficient in Python and experienced with modern ML frameworks and large-scale data processing. You understand how to evaluate ML systems rigorously, including offline metrics, experiment design and interpreting online results. You communicate complex technical ideas clearly and work effectively with cross-functional partners. Why Join Us? At Deliveroo, you'll do work that matters—solving real-world problems in a three-sided marketplace that's constantly evolving. We're food lovers, problem solvers, community builders and more, brought together by a shared drive to make things better. Working here you can expect to: 🔧 Solve meaningful problems at real scale Work on a complex, always-on marketplace that impacts millions every day. 🌱 See your impact, fast Ship, test and improve ideas quickly in a low-hierarchy, high-ownership environment. 🧠 Grow through challenge and ownership Take on big, ambiguous problems and accelerate your career with strong support. 🌎 A culture built for builders High standards, collaboration, flexible working and continuous learning. 💰 Share in the success you help create Competitive salary and equity options, so you're rewarded for the impact you make. ➡️ Want a deeper look at how we build? Check out our Tech Blog. Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, or belief. If you have a disability or long-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience. If you’re excited about making a real impact in a fast-moving marketplace and growing your career alongside ambitious, supportive teams, we’d love to hear from you! Beware of recruitment scams: DASH Brands will never ask you to pay money or share sensitive financial information during hiring — learn more about DASH Brands' legitimate recruiting process at careersatdoordash.com/recruitment-scam-awareness .

Location: New York; Detroit; Canada; Austin; Phoenix; Los Angeles; San Francisco; Dallas Workplace: Remote Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads. The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla. We’re on a mission to transform how companies use data. Come be a part of our journey! ABOUT THE TEAM The AI/ML Engineering team builds and operates ClickHouse's AI and machine learning products end-to-end. This includes the Agentic Data Stack, AI Functions, chDB, the in-Console copilot, and the AI/ML partnerships that distribute them — together with the shared components and expertise that let every other ClickHouse team ship AI in the surfaces they own. Our team is looking for highly skilled and experienced software engineers to join us, who will be responsible for designing, building, and operating the products that make ClickHouse the platform of choice for agents and data scientists. WHAT YOU WILL DO - Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform. - API Architecture: Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services. - Ecosystem Integrations: Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools. - Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation. - Oncall: Participate in the daytime oncall rotation along with the rest of the team. - ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products. WHAT YOU WILL BRING ALONG - 5+ years of software engineering experience in production environments - Exposure to working directly with AI/ML technologies - Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases - A high level of ownership and can drive features from concept to production with minimal supervision - Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders - Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions Nice to have - Familiarity with cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment - Understanding of database systems and data processing pipelines, with ClickHouse experience being a significant plus - Experience with embedding-based systems (retrieval, ranking, recommenders) and the representation-learning ideas underneath them PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Задачи: — Анализировать данные, выбирать подходящие архитектуры (от классического ML до нейросетей) и обучать модели — Искать и тестировать новые подходы и решения, адаптируя их под наши нужды — Самостоятельно разворачивать модели в продакшн-среде — Настраивать рабочее окружение и библиотеки в условиях отсутствия прямого доступа к интернету — Настройка и интеграция локальных LLM Требования: — Уверенное владение Python и ML-стеком (Pandas, Scikit-learn, PyTorch/TensorFlow) — Понимание математики и статистики, стоящую за алгоритмами — Опыт работы с современными архитектурами (NLP, CV или LLM) — Умение упаковывать свои решения в Docker (критично для нашего контура) — Базовое знание Linux и опыт построения API-сервисов — Готовность работать полностью оффлайн в защищенной среде Будет плюсом: — Опыт развертывания локальных языковых моделей (LLM) — Навыки оптимизации кода под конкретное железо (GPU) — Умение работать с SQL-базами данных — Опыт с RAG, inference/serving Условия: — Только оффлайн формат работы (строго закрытый контур) — Работа в стабильном, регламентированном стеке с использованием собственных мощностей (GPU) — Работа с уникальными данными в защищенной среде

Обязанности: • Экспертиза в Linux, Docker. Требования: • Machine learning • Data science • Глубокие знания в различных областях ИИ: Computer vision, NLP, data mining • Опыт разработки Data Science architectures

Location: European Union About Toloka At Toloka AI we create data that powers leading GenAI models and innovations. We work with frontier labs, big tech, renowned AI startups, enterprises and non-profit research organizations worldwide. We use a combination of Experts + Crowd + Tech Platform to teach AI models to reason and evaluate their efficacy and safety. We have experts in more than 50 different domains—from doctors and lawyers to physicists and engineers—and boast one of the most diverse global crowds, representing ove r 100 countries and speaking 40+ languages. We are a well-funded startup with an enviable portfolio of clients including Anthropic, Amazon, Microsoft, Poolside, Recraft, and Shopify. Recently, we secured strategic investment led by Bezos Expeditions and Nebius Group with participation from Mikhail Parakhin, CTO of Shopify and board advisor to leading GenAI companies, who now serves as our Chairman of the Board. Our remote-first team is globally distributed around the world : USA, UK, the Netherlands, Serbia , and more. About the Team We are the ML team inside Toloka — we build the machine-learning products that power the platform itself, so every project running on Toloka is faster, cheaper, and more reliable. A few examples of what we own: Enterprise post-training- we are helping real companies by providing them small LLMs that beat frontier models in quality at a fraction of the cost. Off-the-shelf post-training - we are conducting research on datasets we deliver to clients, showing that training on these datasets will improve their performance. Fine-tuning and RL — adapting frontier and open-source models to Toloka's tasks to hit the right quality at the right cost. Evaluation, benchmarking, cost modeling, and model selection across providers. LLM QA — the core technology behind Toloka's automated quality-check mechanism. Every annotation flowing through Self-Service is reviewed by an LLM agent we design, train, and operate. We own the full chain. The same team designs the ML solution, ships it to production, keeps it running 24/7, analyzes the results coming back from real projects, and feeds that signal into the next iteration. No hand-off between research, engineering, and operations — it's all us. About the Position As a Principal ML Researcher, you will define the overarching technical vision, research strategy, and architecture for Toloka’s core ML and post-training stack. In this high-impact role, you will bridge frontier AI research and large-scale platform engineering. You will lead technical strategy across greenfield post-training paradigms (such as GRPO, process/outcome reward modeling, and RLAIF), architect resilient automated evaluation ecosystems, and set the standard for how foundation models are adapted and served on our platform. As a technical authority, you will mentor senior ML engineers, collaborate directly with executive leadership, and represent Toloka in the broader AI research community. What you’ll do Drive Technical Strategy: Define the multi-quarter strategy and architecture for Toloka’s post-training stack, ensuring our fine-tuning, RL, and evaluation capabilities stay ahead of industry trends. Architect Greenfield Post-Training & RL Pipelines: Spearhead the transition beyond basic SFT into advanced RL frameworks (GRPO, PPO/DPO, reward modeling, Process Reward Models, and RLAIF) to power platform-wide LLM alignment and agent behavior. Pioneer Next-Gen Evaluation Harnesses: Design and calibrate bulletproof automated evaluation ecosystems, including human-aligned LLM-as-a-judge frameworks, dynamic benchmarking suites, and regression control setups. Advance Distillation & Inference Optimization: Lead research initiatives on model distillation, quantization, context compression (e.g., gisting), and speculative decoding to optimize latency-cost trade-offs at platform scale. Shape Agentic Platform Capabilities: Architect autonomous guiding agents and tool-use workflows, establishing foundational frameworks for multi-step reasoning, self-correction, and evaluation-driven feedback loops. Technical Leadership & Mentorship: Elevate the engineering and research bar across the team through architectural design reviews, hands-on mentoring of Senior ML Researchers, and establishing best practices for reproducible ML R&D. Cross-Functional & Community Influence: Partner with Product and Engineering directors to translate complex client challenges into scalable platform architecture; author high-impact research write-ups, blog posts, and external tech talks. What we're looking for 6+ years in ML engineering or applied research, with 3+ years of proven track record leading LLM post-training, fine-tuning, or alignment initiatives at scale. Deep theoretical and practical mastery of LLM alignment: SFT, LoRA/PEFT, RLHF/RLAIF (GRPO, DPO, PPO), reward modeling, and reasoning-oriented post-training. Mastery of LLM Evaluation & Calibration: Proven history of building evaluation pipelines from scratch, addressing LLM-as-a-judge biases, and tightly calibrating automated evals against human gold standards. Expert Distributed Training & Systems Engineering: Strong proficiency in Python, PyTorch, distributed training frameworks (Deepspeed, Megatron, FSDP), and high-performance inference engines (vLLM, TensorRT-LLM). Architectural Vision & Product Leadership: Demonstrated ability to map ambiguous technical challenges into robust platform features, balancing state-of-the-art research with production reliability and cost constraints. Technical Authority: Proven experience acting as a technical leader, mentoring senior researchers/engineers, and influencing cross-functional roadmaps. Language: Fluent spoken and written English (C1), with clear communication skills to articulate complex technical concepts to both internal teams and external clients/community. What we can offer You will be part of an international, dynamic environment that drives innovation and sets new standards in the AI and technology sector. Competitive compensation package including base salary, bonus, and ESOP. Paid PTO and benefits will vary depending on location. We offer a full remote or hybrid model (if you are based in NL or Serbia). IT setup and home office allowances. Equal Opportunity Employer: Toloka is committed to providing equal opportunity and fostering an inclusive environment. We welcome applications from all qualified individuals and do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, gender identity, age, marital status, veteran status, disability, or any other characteristic protected by applicable law. Selection decisions are made based on qualifications, merit, and business need. [Important Notice] Scam Alert Regarding Fake Job Postings It has come to our attention that an individual or group is fraudulently impersonating Toloka to post fake jobs and solicit personal information from applicants. Please be aware: Official Communication: Our recruiting team will only contact you from an official " toloka.ai " email address. We will NEVER use Gmail, Yahoo, Tolokainc, toloka.inc, or other personal or seemingly business email accounts. Our Process: We will never ask for your bank account details, credit card number, or any fees as part of the application or interview process. Official Listings: All legitimate job openings are posted on our official careers page: https://toloka.ai/careers#job-list What to do: If you see a suspicious job posting or have been contacted by someone you suspect is a scammer, please do not provide any personal information. Instead, report the incident to us directly at security@toloka.ai and report the profile/post to LinkedIn.We are taking this matter very seriously and are working with the appropriate parties to resolve it. Thank you for your vigilance! To learn how we collect, use, disclose, and store personal data, check out our Privacy Notice .

Задачи: — Разрабатывать модели прогнозирования, скоринга, детекции аномалий и AI-агентов. — Выводить модели в production и масштабировать их. — Руководить небольшой ML-командой: менторинг, процессы, распределение задач. — Работать напрямую с заказчиками и участвовать в R&D. Требования: — Strong Senior в ML/AI. — Python, статистика, PyTorch/TensorFlow, scikit-learn, MLflow. — Опыт работы с AWS/Azure/GCP. — Системное мышление и желание строить технологии, которые влияют на решения национального уровня. — Команда русскоязычная, знание английского - плюс.

Задачи: — Разработка и развитие модуля Demand Planning — Проектирование и реализация высоконагруженных алгоритмов прогнозирования спроса и анализа рыночных сигналов — Ответственность за компоненты работы с данными и качество прогнозов Требования: — Глубокое знание алгоритмов, структур данных, оценка сложности по Big O — Python (Senior уровень) — Библиотеки анализа данных: Pandas, Scikit-learn, Statsmodels — ООП и принципы проектирования вычислительных систем — SQL и реляционные базы данных (PostgreSQL) — Юнит- и интеграционное тестирование, методология тестирования — Будет плюсом: математическая статистика и основы машинного обучения (линейные модели, Random Forest, LSTM) — Работа с временными рядами: очистка, прогнозирование, кросс-валидация — Библиотеки градиентного бустинга: XGBoost, LightGBM, CatBoost — Глубокое обучение для временных рядов: LSTM, Prophet, DeepAR — Работа в Unix-подобных системах, понимание принципов CI/CD — C / C++ для оптимизации критичных узлов системы — Высшее техническое образование (математика, информационные технологии, физика, прикладные науки) или релевантный опыт — Сертификации по Python, анализу данных, машинному обучению или архитектуре программного обеспечения Условия: — Полный рабочий день — Можно удалённо — Оформление: только ИП (не СЗ, не ТК) — Долгосрочный проект

Location: Colombia, Huila, Colombia; São Paulo, Brazil About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Senior ML & Data Engineer — Data Quality & Sensitive Data Compliance This is a full-time remote role based in Brazil or Colombia. About the role Enterprise data flows through our connectors, gets processed, and passes through a sanitization layer before anything downstream touches it. Two things have to be true at every step: the data is what we think it is, and no sensitive information — PII, PHI, company identifiable information (CII), or financial data — gets through. You'll own both. You'll do this primarily by building the machine learning that detects sensitive entities in text and image data and replaces them consistently at scale. This is a hands-on IC engineering role with a QA mindset. You'll build the detection models, validation infrastructure, adversarial test sets, and audit processes that let us make strong claims about data quality and de-identification performance — and back them up with evidence. You'll work closely with a senior ML lead, with no client-facing responsibilities. What you'll do Data Quality Run deep dives into enterprise data to assess quality: topic coherence across connectors, domain depth within connectors, completeness, and consistency Design and automate validation suites for data pipelines — schema checks, completeness, drift detection, and reconciliation across raw → processed → sanitized stages Surface and characterize quality issues in ways that engineering and product can act on Sensitive data compliance (PII / PHI / CII / financial) Design, train, and evaluate ML models (NER and other approaches) that detect sensitive entities across text and image-based documents such as scans, invoices, and presentations Build replacement pipelines that substitute detected entities with coherent alternatives, so the same entity always maps to the same replacement across every file in a corpus and the data stays useful Run these algorithms over large volumes of data to prepare it for downstream agentic task building Build adversarial test sets for de-identification across all sensitive data classes: edge cases, obfuscated identifiers, multilingual entities, OCR noise, and formats designed to slip past detectors Cover company identifiable information specifically — organization names and aliases, domains and email patterns, internal project and system names, org charts, vendor and partner relationships, contract terms, and any combination of details that could re-identify the source enterprise Cover financial data — account and routing numbers, card numbers, revenue and pricing figures, transaction records, tax IDs, and financial statements Measure and report de-identification performance by data class — entity-level precision and recall, leak rates, false-negative audits, and replacement consistency Implement regression gates in CI/CD so no pipeline change ships without passing data quality and sensitive-data checks Run sampling-based human-in-the-loop audits and maintain the audit trail as compliance evidence Partner with engineering on root-cause analysis when inconsistencies or leaks are found, and drive fixes to closure What we're looking for About 4 to 5 years of hands-on machine learning experience, with ML as your primary background Strong Python for ML development and data validation (pytest, Great Expectations, Pandera, or similar) Solid SQL and experience validating data across pipeline stages Familiarity with sensitive data categories and the relevant standards — HIPAA Safe Harbor for PHI, GDPR/LGPD for PII, PCI DSS for cardholder data, and confidentiality/NDA obligations for company information Experience building and testing NER or other ML-based detection systems: building labeled eval sets, computing precision/recall, handling non-determinism Understanding of re-identification risk — how seemingly innocuous details combine to reveal an organization or individual Comfort with ambiguity and a fast-moving environment A skeptical, detail-oriented approach — you assume things are broken until you've proven otherwise Nice to have Computer vision and OCR experience, especially building, scaling, and evaluating document pipelines for contracts, statements, invoices, presentations, and internal documents Hands-on experience with financial or healthcare data, including the privacy requirements specific to those industries Startup experience Auditing LLM or VLM outputs Synthetic sensitive-data generation (PII, PHI, company and financial records) Familiarity with the GCP data stack (BigQuery, GCS, Cloud Run jobs) and CI/CD integration Experience handling multi-tenant enterprise data with strict customer confidentiality requirements Compliance reporting or working with auditors Why this role matters Our enterprise customers trust us with their data on the condition that it can never be traced back to them. Every downstream model, dashboard, and customer commitment depends on the data being clean and the sanitization layer being airtight. When you find a leak, you've prevented an incident. When you prove there isn't one, you've earned the trust that lets the rest of the company move fast. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: Belgrade; London; Berlin Workplace: Hybrid Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. Our work spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective. We control the full stack: the models, the agent harnesses, and the search infrastructure underneath. That gives us the freedom to develop new approaches across all three training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work. RESPONSIBILITIES - Push search and agent quality forward through improvements to models, training data, tools, and system design. - Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion. - Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively. - Design and build agent harnesses around the tools, context management, execution environments, and orchestration that support reliable work over many steps. - Improve retrieval and ranking models and the search interfaces agents use to find and assess information. - Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements. - Own experiments end to end, from a clear hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost. - Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production. QUALIFICATIONS - A strong track record of building and shipping ML systems, with deep experience in one or more of LLM post-training, reinforcement learning, search and retrieval, or agent systems. - Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems. - Experience designing rigorous evaluations, diagnosing failures, and translating experimental results into practical improvements. - Comfort with open-ended problems that require both research judgment and hands-on engineering. - A strong sense of ownership, curiosity, and the drive to carry an idea through to a working system. NICE TO HAVE - Experience with training models to use tools or complete tasks over many steps. - Multi-agent training, coordination, or evaluation. - Building agent harnesses, distributed training systems, or scalable inference infrastructure. - Large-scale retrieval, ranking, or recommendation systems.

Location: Belgrade; London; Berlin Workplace: Hybrid Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. Our work spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective. We control the full stack: the models, the agent harnesses, and the search infrastructure underneath. That gives us the freedom to develop new approaches across all three training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work. RESPONSIBILITIES - Push search and agent quality forward through improvements to models, training data, tools, and system design. - Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion. - Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively. - Design and build agent harnesses around the tools, context management, execution environments, and orchestration that support reliable work over many steps. - Improve retrieval and ranking models and the search interfaces agents use to find and assess information. - Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements. - Own experiments end to end, from a clear hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost. - Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production. QUALIFICATIONS - A strong track record of building and shipping ML systems, with deep experience in one or more of LLM post-training, reinforcement learning, search and retrieval, or agent systems. - Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems. - Experience designing rigorous evaluations, diagnosing failures, and translating experimental results into practical improvements. - Comfort with open-ended problems that require both research judgment and hands-on engineering. - A strong sense of ownership, curiosity, and the drive to carry an idea through to a working system. NICE TO HAVE - Experience with training models to use tools or complete tasks over many steps. - Multi-agent training, coordination, or evaluation. - Building agent harnesses, distributed training systems, or scalable inference infrastructure. - Large-scale retrieval, ranking, or recommendation systems.

Location: Paris 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 ROLE We are hiring an LLM Scientist to push the boundaries of AI-driven scientific discovery. You will specialize in developing and optimizing large language models (LLMs), agentic AI, and deep learning frameworks to solve open problems in the natural sciences: Biology, Chemistry, Physics, and/or Materials Science. Your work will empower scientists with cutting-edge AI tools and accelerate research through: - Model-centric innovation: Improving LLMs’ capabilities to tackle complex scientific problems (e.g., literature synthesis, autonomous experimentation, rigorous derivations). - Cross-disciplinary collaboration: Partnering with Natural sciences domain experts to translate AI advancements into real-world scientific impact. WHAT YOU WILL DO - AI/ML Research: Participate in end-to-end training of LLM-based solutions, with a focus on scalability, generalization, and scientific applicability. - Data creation: Design original workflows to accumulate high quality scientific data at scale, either through synthetic or crowd sourced pipelines. - Algorithm Design: Use and improve reinforcement learning, or agentic systems tailored to Natural sciences challenges (e.g., modeling resolution, material property prediction, advanced data analysis and coding). - Interdisciplinary Bridge: Work closely with domain scientists to identify AI opportunities and refine models based on feedback. - Tooling & Infrastructure: Contribute to company infrastructure to improve usability for model training in scientific contexts and open-source AI tools for science for non-AI experts. - Autonomous Innovation: Set a research agenda for AI in science, exploring high-risk/high-reward ideas.   WHAT WE'RE ARE LOOKING FOR - AI/ML Expertise: Strong background in LLMs, deep learning, or agentic AI, with a track record of applying these to real-world problems (publications, open-source contributions, or deployed systems) – at the minimum extensive experience in computer science and ideally experience scaling synthetic data generation. - State-of-the-art Knowledge: Familiarity with the latest advancements in LLM architectures and post training. - Technical Depth: Hands-on experience with PyTorch/TensorFlow/JAX, model optimization, and scaling techniques (e.g., fine-tuning, RLHF). - Scientific Capability: Ability to understand 1 Natural Science domain at a high level (e.g., graduate level specialization + general undergrad knowledge) to design effective AI solutions. - Engineering Rigor: Proficiency in scientific computing (e.g., numerical methods, HPC) and software best practices. - Collaboration: Experience translating technical AI concepts for non-experts (e.g., scientists, engineers) and iterating based on user needs. - Mindset: Low ego, team player, and eager to tackle operational challenges (e.g., data pipelines, model deployment). MOTIVATIONAL PARAGRAPH When applying for the AI for Science team at Mistral, please describe in a short paragraph a potential project that you would like to contribute to the team, why it is challenging and interesting in general, and more specifically for Mistral. Please also highlight the specific skill set that you can bring to the team. 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.