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Location: Athens, Greece; Thessaloniki, Greece About the Role We are seeking an AI/ML Engineer - python developer to join our software development team. You will work on challenging international projects, using cutting-edge technology to deliver ML and AI solutions for major international public organizations. The role offers rapid career growth and real exposure to high-impact, large-scale work. What You Will Do Implement advanced ML solutions and model deployment strategies (MLOps); Build advanced Generative AI components and pipelines, including RAG architectures, orchestration layers and evaluation frameworks; Contribute to AI governance, observability, monitoring and responsible AI practices; Contribute to the design and documentation AI solutions; Contribute to python implementation tasks and implementation of micro-services using frameworks such as FastAPI, Flask, Pydantic. etc. Requirements A university degree in IT or a related field; Very good knowledge of python, pandas, numpy, scipy, matplotlib, seaborn; Good knowledge of  FastAPI, Flask, Pydantic, SQL Alchemy; Hands-on experience in use and integration with leading LLMs and SLMs; Familiarity with AI frameworks such as LangChain, LangGraph, Haystack; Familiarity with major ML frameworks such as PyTorch and TensorFlow, and with model formats such as ONNX; Excellent command of English, written and spoken. Nice to Have Experience designing and implementing AI/NLP pipelines (AI Ops / NLP Ops); Familiarity with AWS SageMaker and / or  Azure Machine Learning platform; Familiarity with cloud platforms such as AWS or Azure; Familiarity with Big Data systems such as Spark (PySpark) and/or Databricks. Benefits We believe in rewarding talent and dedication. Here's what you can expect as part of our team: Competitive full-time salary; Private Health Coverage on the Company’s group program; Flexible Working Hours; Top-of-the-Line Tools; Professional Development: Benefit from language courses, specialized training, and continuous learning opportunities; Career Growth: Work with some of the most innovative and exciting specialists in the industry; Dynamic Work Environment: Thrive in a setting that offers challenging goals, autonomy, and mentoring, fostering both personal and company growth. If you want an exciting challenge, work with some of the coolest technologies, and enjoy your time doing it, then join us! Submit your detailed CV in English, quoting reference: ( JA/ME-PD/11/26 ). You may also consider all our other open vacancies by visiting the career section of our website ( www.eurodyn.com ) and follow us on Twitter (@EURODYN_Careers) and LinkedIn. EUROPEAN DYNAMICS (ED) ( www.eurodyn.com ) is a leading European Software, Information, and Communication Technologies company, operating internationally (Athens, Brussels, Luxembourg, Copenhagen, Berlin, Stockholm, London, Nicosia, Valetta, Vienna, Den Haag, Hong Kong, etc.) The company employs over 1100 engineers, IT experts, and consultants (around 3% PhD, 41% MSc, and 54% BSc). We design and develop software applications using integrated, state-of-the-art technology. Our current IT projects have a value exceeding 300 million EURO. EUROPEAN DYNAMICS is a renowned supplier of IT services to European Union Institutions, international organizations, European Agencies, and national government Administrations in 40 countries and 4 continents. As part of our dedication to the diversity of our workforce, we are committed to Equal Employment Opportunity without regard for race, colour, 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 at 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 with the Policy.

Location: Thuwal, Saudi Arabia This position is for building, deploying and evaluating Client's Arabic AI tutor for mathematics and computer science: preparing Arabic training data, fine-tuning, aligning and evaluating LLMs on Shaheen III and Ibex, and integrating the tutor into the learning platform for a pilot in at least 10 Saudi schools. The position is part of the RDIA-funded project 5870, “An AI-Powered Arabic Tutoring Platform for STEM Education in the Kingdom.” Two engineers will be hired into this role. Key Responsibilities Prepare Arabic training datasets from educational materials and consented platform interactions. Fine-tune, align, and evaluate commercial and open-source LLMs using Shaheen III and Ibex computing platforms. Develop Arabic STEM benchmarks, preference-training methods, and student-safety guardrails. Integrate AI tutoring capabilities into the learning platform, including Arabic right-to-left interfaces. Deploy and optimize models for reliable, low-latency real-time interactions. Support school pilot implementation, evaluation, and teacher feedback collection. Ensure student data privacy and compliance with Saudi data protection requirements. Contribute to research publications, technical reports, and project deliverables. Qualifications & Experience MSc in Computer Science, Artificial Intelligence, or a related field; alternatively, a BSc with at least one year of relevant experience. Hands-on experience in LLM fine-tuning, evaluation, and AI application deployment. Professional proficiency in Arabic and English. Experience with RLHF/DPO, educational technology, or mathematics/programming education is preferred. Technical Skills Advanced Python and PyTorch. Hugging Face Transformers, PEFT, and TRL. Distributed GPU training using DeepSpeed or FSDP. LLM evaluation, reward modeling, and preference optimization. Arabic NLP, data pipelines, and OCR. Model serving and inference optimization using tools such as vLLM. APIs, Docker, Git, CI/CD, and production deployment.

Location: Sydney 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: Technical Lead, Applied AI   Mistral AI is seeking a Technical Lead, Applied AI to drive the technical strategy, execution, and delivery of complex AI solutions for our enterprise customers. In this role, you will lead a project teams of Applied AI Engineers, ensuring the successful deployment of Mistral AI products and the development of high-impact, scalable AI use cases.   You will act as the primary technical point of contact for our most strategic customers, guiding them through the entire lifecycle—from pre-sales to post-implementation—while collaborating closely with research, product, and engineering teams to shape the future of our offerings.   As a Technical Lead, you will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral’s technological vision.   What you will do   - Deliver as an IC the critical lines of codes of our complex projects, you’ll be hands-on and de-risk the critical parts of our complex projects. You’ll stay deeply involved in coding, reviewing, and optimizing AI solutions. - Lead technical teams of Applied AI Engineers, providing mentorship, technical guidance, and best practices for deploying state-of-the-art GenAI applications across industries. - Lead technical discussions during pre-sales, translating customer requirements into actionable solutions and communicating Mistral’s technological advantages to diverse stakeholders. - Design and oversee the implementation of complex AI systems, including fine-tuning, RAG, agentic workflows, and custom LLM applications, ensuring alignment with Mistral’s product roadmap and open-source initiatives. - Drive innovation by identifying emerging trends in AI, evaluating new tools and methodologies, and championing best practices for fine-tuning, inference, and deployment. - Work closely with product managers, researchers, and engineers to ensure seamless integration of customer feedback into Mistral’s product development cycle.   About you - You hold a PhD or Master’s degree in AI, Machine Learning, Computer Science, or a related field. - You have 7/8+ years of experience in AI/ML, with at least 2+ years in a technical leadership role (e.g., Tech Lead, Engineering Manager, or Solutions Architect) focused on AI products or enterprise solutions. - You have a proven track record of leading teams to deliver complex AI projects, from prototyping to production, in industries such as tech, finance, healthcare, or industrial automation. - You possess deep expertise in fine-tuning LLMs, advanced RAG, agentic systems, and deploying NLP applications at scale. - You are proficient in Python, PyTorch, and modern AI frameworks (e.g., LangChain, Hugging Face). Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools is a plus. - You have strong software engineering skills, including API design, backend/full-stack development, and system architecture. - You excel in technical communication, with the ability to articulate complex concepts to both technical and non-technical audiences, including executives and engineers. - You thrive in fast-paced, collaborative environments and are passionate about mentoring and growing technical talent.   Ideally, you have: - Contributed to open-source projects, particularly in the LLM or AI space. - Experience in customer-facing roles (e.g., Solutions Architect, Customer Engineer, or Technical Product Manager) with a focus on enterprise AI adoption. - A track record of driving technical strategy and influencing product direction based on customer needs and market opportunities.   Why join us? You’ll have the opportunity to shape the future of AI adoption in enterprises, work with a world-class team, and contribute to open-source projects that impact millions. If you’re excited about leading technical innovation and solving real-world challenges with AI, we’d love to hear from you!   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.

Требования: — Основное: Python, FastAPI, Git, Docker. — AI фреймворки: OpenAI SDK, LangChain / LangGraph, LlamaIndex. — Автоматизация: n8n, Make, REST API, Webhooks. — БД: SQL, PostgreSQL, Vector DBs (Redis / NoSQL как плюс). — Высшее техническое образование (CS, ИИ, математика, анализ данных) или сильный практический бэкграунд и портфолио реализованных AI-продуктов. — Опыт вывода AI-решений в реальный Production (логирование, обработка ошибок, мониторинг). — Плюсом будет: работа с multi-agent системами, Voice AI, Computer Vision / Multimodal AI. — Опыт fine-tuning моделей и локального деплоя open-source LLM (Hugging Face, PyTorch, Kubernetes). — Английский (чтение документации, написание English prompts). Локация: Минск, офис.

Требования к специалисту: — Уверенный опыт работы с LLM/SLM (Few-shot learning, prompt engineering) и BERT (классификация сценариев и интентов). — Практический опыт finetuning LLM (PEFT, LoRA/QLoRA) под узкие прикладные задачи. — Уверенный дебаг LLM: умение быстро понять, почему модель ответила не так, и исправить это промптом, гиперпараметром или файнтюном. — Опыт дообучения и оптимизации TTS-моделей. — Опыт работы и интеграции современных ASR-систем (в частности, GigaAMv2). — Опыт работы как с графовыми/сценарийными диалоговыми движками, так и с LLM-агентами. — Высокая скорость диагностики проблем в инференсе, локализации багов и принятия инженерных решений. — Уверенное владение Python (PyTorch, huggingface, фреймворки инференса) и Go. — Опыт работы с оптимизацией инференса под low-latency (vLLM, TensorRT-LLM, Triton Inference Server, ONNX Runtime). — Практика работы со потоковым аудио в реальном времени (streaming ASR/TTS, gRPC-контракты). — GoLang: базовое понимание дизайна сервисов (SOLID, DRY, KISS); — типизация (строки, числа, слайсы и мапы); — примитивы синхронизации и конкуренция (горутины, каналы); — указатели и работа с ними; — observability. — Будет плюсом: • опыт работы с gRPC. • умение настроить инфраструктуру (k8s/inventory). • знания работы TCP. Задачи: — Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. — Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.). — Оперативное принятие архитектурных решений и их самостоятельная реализация в коде. — Работа с диалоговым стеком и NLP: поддержка и быстрая отладка few-shot классификации сценариев на базе Qwen и классификаторов на BERT. — Учёт краевых случаев, исправление ошибочных срабатываний интентов. — При необходимости — finetuning локальных LLM (извлечение сущностей, удерживание инструкций). — Работа с речевым стеком: интеграция и поддержка ASR на базе GigaAMv2 (отладка качества распознавания, работы VAD и передачи текста дальше по пайплайну). — Дообучение и настройка инференса собственных и опенсорсных TTS и систем клонирования голоса для достижения нужного качества звучания и скорости работы.

Требования к специалисту: — Уверенный опыт работы с LLM/SLM (Few-shot learning, prompt engineering) и BERT (классификация сценариев и интентов). — Практический опыт finetuning LLM (PEFT, LoRA/QLoRA) под узкие прикладные задачи. — Уверенный дебаг LLM: умение быстро понять, почему модель ответила не так, и исправить это промптом, гиперпараметром или файнтюном. — Опыт дообучения и оптимизации TTS-моделей. — Опыт работы и интеграции современных ASR-систем (в частности, GigaAMv2). — Опыт работы как с графовыми/сценарийными диалоговыми движками, так и с LLM-агентами. — Высокая скорость диагностики проблем в инференсе, локализации багов и принятия инженерных решений. — Уверенное владение Python (PyTorch, huggingface, фреймворки инференса) и Go. — Опыт работы с оптимизацией инференса под low-latency (vLLM, TensorRT-LLM, Triton Inference Server, ONNX Runtime). — Практика работы со потоковым аудио в реальном времени (streaming ASR/TTS, gRPC-контракты). — GoLang: базовое понимание дизайна сервисов (SOLID, DRY, KISS); типизация (строки, числа, слайсы и мапы); примитивы синхронизации и конкуренция (горутины, каналы); указатели и работа с ними; observability. — Будет плюсом: опыт работы с gRPC; умение настроить инфраструктуру (k8s/inventory); знания работы TCP. Задачи: — Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. — Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.). — Оперативное принятие архитектурных решений и их самостоятельная реализация в коде. — Работа с диалоговым стеком и NLP: поддержка и быстрая отладка few-shot классификации сценариев на базе Qwen и классификаторов на BERT. — Учёт краевых случаев, исправление ошибочных срабатываний интентов. — При необходимости — finetuning локальных LLM (извлечение сущностей, удерживание инструкций). — Работа с речевым стеком: интеграция и поддержка ASR на базе GigaAMv2 (отладка качества распознавания, работы VAD и передачи текста дальше по пайплайну). — Дообучение и настройка инференса собственных и опенсорсных TTS и систем клонирования голоса для достижения нужного качества звучания и скорости работы.

Location: Berlin, Germany About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: London, United Kingdom About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Lugano, Switzerland Workplace: remote Join Tether and Shape the Future of Digital Finance At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction. Innovate with Tether Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT , relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services. But that’s just the beginning: Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities. Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET , our flagship app that redefines secure and private data sharing. Tether Education : Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity. Tether Evolution : At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways. Why Join Us? Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry. If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you. Are you ready to be part of the future? About the job As a member of our AI research team, you will drive innovation in model compression and efficient deployment for advanced multimodal AI systems, including large language models (LLMs) and vision-language models (VLMs). Your work will focus on reducing model footprint and computational cost while preserving accuracy, enabling high-performance AI to run efficiently across resource-constrained edge devices. You will apply and advance compression techniques such as quantization, knowledge distillation, and pruning to streamline complex multimodal architectures that integrate text, images, and audio. We expect you to have deep expertise in model compression methods and a strong background in multimodal model architectures. You will adopt a hands-on, research-driven approach to develop, test, and implement novel compression strategies that balance model size, latency, throughput, and accuracy. Your responsibilities include building robust compression pipelines, establishing performance and fidelity metrics, and addressing bottlenecks in production inference. The ultimate goal is to deliver scalable, low-memory, low-latency AI systems on edge devices (i.e., smartphones) that maintain high fidelity and tangible real-world value. Responsibilities Apply low-bit quantization to reduce model size and inference latency for generative AI models (LLMs, VLMs, multimodal) while maintaining accuracy and output quality. Leverage knowledge distillation to transfer capabilities from larger teacher models to smaller student models, enabling efficient multimodal reasoning across text, image, and audio inputs. Implement pruning techniques to remove redundant parameters and attention heads, reducing computational overhead without sacrificing task performance. Analyze trade-offs between model efficiency (size, latency, memory) and accuracy across quantization, distillation, and pruning methods; propose improvements based on empirical findings. Research and apply mixed-precision quantization and other advanced compression strategies (e.g., adaptive pruning schedules, distillation with intermediate feature matching) to optimize the accuracy–performance balance. Stay current with the latest research in model compression, including emerging techniques for multimodal and generative architectures. Document methodologies, experiments, and results clearly to support reproducibility, internal collaboration, and stakeholder communication. Author technical papers and publish findings in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, AAAI) to advance the field of model compression for multimodal AI. List the qualifications, certifications, and experience needed for the role. You can split this into mandatory and preferred sections if needed. A degree in Computer Science  or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences). Experience with PyTorch deep learning frameworks or equivalent frameworks Hands-on experience with model quantization including both Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ). Research and hands-on experience with knowledge distillation for compressing large models into smaller, efficient ones. Research and hands-on experience with model pruning for compressing large models into smaller, efficient ones. Solid understanding of neural network architectures and training processes – Including transformers (e.g., LLMs, VLMs), backpropagation, optimization, and fine-tuning techniques. Familiarity with C++ is a plus (especially for implementing low-level quantization kernels or inference optimizations). Important information for candidates Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles: Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/ Verify the recruiter’s identity. All our recruiters have verified LinkedIn profiles. If you’re unsure, you can confirm their identity by checking their profile or contacting us through our website. Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms. Double-check email addresses. All communication from us will come from emails ending in @ tether.to or @ tether.io We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately. When in doubt, feel free to reach out through our official website.

Задачи: — Разработка, деплой и оптимизация мультимодальных моделей восприятия и рассуждения для физических роботов. Требования: — Опыт работы с технологиями TensorRT, Jax, ONNX, ROS2, PyTorch, Python, Docker, C#, Go, NVIDIA Jetson. Условия: — Работа в офисе в Сан-Франциско. — Зарплата от $130,000 до $180,000 в год.

Задачи: — Разрабатывать, поддерживать и владеть ключевыми моделями компьютерного зрения для обнаружения мошенничества, формирующими продуктовую линейку Jumio. — Проектировать и внедрять методы машинного обучения, глубокого обучения и классического компьютерного зрения, ориентированные на выявление мошенничества. — Проводить исследования для поддержки внедрения передовых алгоритмов. — Развёртывать модели как AWS SageMaker endpoints или непосредственно на устройствах. — Следить за последними достижениями в области машинного обучения, глубокого обучения и компьютерного зрения, изучая академические публикации и посещая отраслевые конференции. — Работать в тесном сотрудничестве с инженерами и менеджерами продуктов в Agile-среде. Требования: — Степень бакалавра или магистра в области компьютерных наук, науки о данных, машинного обучения или смежных областях. — Минимум 6+ лет коммерческого опыта в области машинного обучения/глубокого обучения (не включая время обучения, 2 года с магистерской степенью). — Опыт обучения моделей глубокого обучения для обнаружения дипфейков, синтетических данных или данных, сгенерированных ИИ. — Глубокое понимание преобразования метрик машинного обучения в продуктовые цели. — Практический опыт работы с PyTorch или TensorFlow, SKLearn и разработки производственного кода на Python. — Опыт внедрения лучших практик мониторинга и поддержки моделей машинного обучения в продакшене. — Сильные коммуникативные и аналитические навыки. Желательно: — Опыт работы с LLM или VLM. — Опыт работы с генерацией изображений или видео. — Опыт работы с облачными платформами AWS и GCP. — Опыт работы в глобальных организациях с несколькими часовыми поясами. Условия: — Полная занятость. — Удалённая работа из Индии. О компании: Jumio — B2B технологическая компания, специализирующаяся на борьбе с онлайн-мошенничеством, отмыванием денег и финансовыми преступлениями, предоставляющая решения для онлайн-верификации личности, eKYC и AML с использованием ИИ, биометрии, машинного обучения и автоматизации. Контакт для вопросов по конфиденциальности данных: privacy@jumio.com.

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. — Архитектурно проектировать, 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 путь моделей до продакшена. — Оптимизировать модели для низкой задержки вывода (квантование, дистилляция, TensorRT/ONNX) и управлять деплоем на AWS. — Наставлять ML-инженеров, проводить ревью кода и дизайна, продвигать технические лучшие практики в команде компьютерного зрения. Требования: — Сильный опыт в машинном обучении, специализация в биометрии или анализе лиц. — Глубокие знания в компьютерном зрении и биометрии, особенно в распознавании лиц. — Понимание источников алгоритмического смещения 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). Плюсы: — 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 или мобильные устройства 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.

Задачи: — Владение AI-функциями от концепции до продакшена в высоконагруженной среде (50k RPS, 450M пользователей). — Работа с LLM и обучение специализированных моделей для приложений в реальном времени. — Тесное сотрудничество с продакт-менеджерами для реализации эффективных решений. — Оценка производительности моделей и адаптация подходов на основе реальных данных. Требования: — Опыт разработки ПО 5+ лет с практической работой в ML/AI. — Подтверждённый опыт работы с LLM: контекстная инженерия, использование инструментов и адаптация моделей. — Сильные навыки обучения кастомных моделей на сложных производственных данных. — Владение Python и PyTorch, понимание практик продакшен-инжиниринга. — Английский язык на уровне Intermediate или выше. Желательно: — Опыт обработки видео/аудио в реальном времени. — Знакомство с рекомендательными системами и онлайн-экспериментами. — Знание Docker, Kubernetes, GCP и инфраструктуры машинного обучения. Условия: — Конкурентная зарплата, бонусы и опционы (все являются партнёрами). — Официальное трудоустройство на Кипре с поддержкой визы для сотрудника и супруга. — Офис в центре Лимассола с питанием и командной культурой. — Медицинская страховка (100% для сотрудника, 75% для семьи). — Возможности профессионального роста и корпоративные семейные мероприятия. Формат работы: офис, Лимассол.

Location: US, San Diego, CA 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 As Director of Autonomy at Humanoid, you will lead the team that teaches our bipedal platform to perform locomanipulation tasks in homes and service environments: tidying a room, loading and unloading a dishwasher, putting groceries away, folding laundry, and similar tasks. The output of your team is capabilities that run reliably on real robots and pass our evals, not papers or one-off demos. This is a player-coach role: you own the roadmap, hiring, and delivery, while also training policies, curating data, and debugging failures on the robot yourself. That hands-on work is how you lead: it's how you find what slows the team down and fix it for everyone. This role sits at the intersection of applied deep learning, robotics, and people leadership, working closely with the Controls team on whole-body teleoperation and combining RL controllers with learned policies, and with the Core Technology team on foundation model pre-training for five-finger dexterity and RL methods. If you have hands-on experience with real robot hardware, a track record in applied deep learning, and have led engineers doing this kind of work, we want to hear from you. What You'll Do - Own The Capability Roadmap: Work with product to identify use-case priority, choose the execution strategy, define the evals, and deliver on the agreed timeline. - Lead The Team: Hire, develop, and manage a team of robot learning engineers; set engineering and experimental standards; run day-to-day prioritization. - Co-Own The System 0/1 Interface: Agree with the Controls team on what the policy commands (end-effector targets, root velocity, pelvis height, foot targets, hand joint targets), debug failures across that boundary, and push requirements into the whole-body control roadmap. - Partner On Data Strategy: Specify what good data looks like for bipedal locomanipulation with data collection and external acquisition organizations, ensure diversity and coverage, iterate on operator instructions, and supervise collection on the biped. - Drive Sim-To-Real Transfer: Work with the Simulation team to set up RL training on the digital twin for bipedal tasks, then iterate on reward and simulation quality to ensure transfer to the real world. - Shape Base Model Development: Feed bipedal requirements — embodiment coverage in the pretraining mixture, action representations, latency budgets — into the VLA Core Technology team's base model development, and adopt new base models as they land. - Inform Hardware Design: Interface with the Hardware Design team so findings about hands, arms, and legs on the current generation are reflected in future designs. - Report On Progress: Communicate progress to leadership in terms of capabilities working on the robot, eval results, and failure analysis. WHAT WE'RE LOOKING FOR - 5+ years working on robots (industry or research) with shipped artifacts to show for it, including learned manipulation or locomanipulation policies deployed on real hardware. - Experience leading a team of 5+ engineers doing applied robot learning, including hiring and people management, with capabilities delivered to a deadline. - Hands-on today: you've trained and debugged policies within the last year and intend to keep doing so, and can curate data, post-train a policy, and diagnose failures without delegating that work. - Experience post-training VLA-class policies (autoregressive, diffusion, or flow-matching based) via behavior cloning and/or RL. - Solid understanding of the modern teleoperation and low-level control stack — whole-body control, inverse kinematics, RL locomotion controllers — and the ability to tell whether a failure lives in the policy, the controller, or the data. - Familiarity with deep learning infrastructure: streaming datasets, checkpointing and state management, distributed training, PyTorch or JAX. - Strong grounding in modern software engineering practices, with the ability to document experiments clearly and communicate trade-offs crisply to engineers and leadership. NICE TO HAVE - Experience with multi-fingered dexterous hands: teleoperation and retargeting, tactile sensing, in-hand manipulation. - Experience with legged platforms: bipedal locomotion, whole-body control, humanoid teleoperation. - Experience applying RL to robotics problems in simulation or on real robots. - Familiarity with Physical Intelligence (π) models, NVIDIA GR00T, or similar open VLA frameworks. - Publications at top-tier robotics or deep learning conferences (CoRL, RSS, ICRA, NeurIPS) or equivalent open-source contributions. WHAT WE OFFER - Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources. - Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays. - 401(k) retirement plan with 4% employer match. - Competitive equity: stock options with meaningful upside as we scale. - Free daily catered lunch, snacks, and drinks in‑office. - Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics. - Freedom to influence the product and own key initiatives. For this role in California, the expected base salary range is $300,000-$450,000 USD per year; your placement in that range depends on how experience maps to our internal leveling.

Location: London Workplace: OnSite ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. About The Job:   Mistral AI is seeking a Technical Lead, Applied AI to drive the technical strategy, execution, and delivery of complex AI solutions for our enterprise customers. In this role, you will lead a project teams of Applied AI Engineers, ensuring the successful deployment of Mistral AI products and the development of high-impact, scalable AI use cases.   You will act as the primary technical point of contact for our most strategic customers, guiding them through the entire lifecycle—from pre-sales to post-implementation—while collaborating closely with research, product, and engineering teams to shape the future of our offerings.   As a Technical Lead, you will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral’s technological vision.   What you will do   - Deliver as an IC the critical lines of codes of our complex projects, you’ll be hands-on and de-risk the critical parts of our complex projects. You’ll stay deeply involved in coding, reviewing, and optimizing AI solutions. - Lead technical teams of Applied AI Engineers, providing mentorship, technical guidance, and best practices for deploying state-of-the-art GenAI applications across industries. - Lead technical discussions during pre-sales, translating customer requirements into actionable solutions and communicating Mistral’s technological advantages to diverse stakeholders. - Design and oversee the implementation of complex AI systems, including fine-tuning, RAG, agentic workflows, and custom LLM applications, ensuring alignment with Mistral’s product roadmap and open-source initiatives. - Drive innovation by identifying emerging trends in AI, evaluating new tools and methodologies, and championing best practices for fine-tuning, inference, and deployment. - Work closely with product managers, researchers, and engineers to ensure seamless integration of customer feedback into Mistral’s product development cycle.   How We Work in Applied AI   - We care about people and outputs. - What matters is what you ship, not the time you spend on it - Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. - Always ask why. The best solutions come from deep understanding, not from copying what worked before - We say what we mean. Feedback is direct, timely, and given because we care. - No politics. Low ego, high standards. - We embrace an unstructured environment and find joy in it.   About you - You are fluent in English. - You hold a PhD or Master’s degree in AI, Machine Learning, Computer Science, or a related field. - You have 7/8+ years of experience in AI/ML, with at least 2+ years in a technical leadership role (e.g., Tech Lead, Engineering Manager, or Solutions Architect) focused on AI products or enterprise solutions. - You have a proven track record of leading teams to deliver complex AI projects, from prototyping to production, in industries such as tech, finance, healthcare, or industrial automation. - You possess deep expertise in fine-tuning LLMs, advanced RAG, agentic systems, and deploying NLP applications at scale. - You are proficient in Python, PyTorch, and modern AI frameworks (e.g., LangChain, Hugging Face). Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools is a plus. - You have strong software engineering skills, including API design, backend/full-stack development, and system architecture. - You excel in technical communication, with the ability to articulate complex concepts to both technical and non-technical audiences, including executives and engineers. - You thrive in fast-paced, collaborative environments and are passionate about mentoring and growing technical talent.   Ideally, you have: - Contributed to open-source projects, particularly in the LLM or AI space. - Experience in customer-facing roles (e.g., Solutions Architect, Customer Engineer, or Technical Product Manager) with a focus on enterprise AI adoption. - A track record of driving technical strategy and influencing product direction based on customer needs and market opportunities.   Why join us? You’ll have the opportunity to shape the future of AI adoption in enterprises, work with a world-class team, and contribute to open-source projects that impact millions. If you’re excited about leading technical innovation and solving real-world challenges with AI, we’d love to hear from you! 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: Amsterdam Workplace: OnSite ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. About The Job   Mistral AI is seeking a Applied AI Engineer to facilitate the adoption of its products among customers and collaborate with them to address complex technical challenges.   The Applied AI team is Mistral's customer-facing technical organization. We work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact.   Our team combines deep ML expertise with strong customer engagement skills, operating like startup CTOs who own end-to-end project execution. By joining the team you'll will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral's technological vision.   What you will do   - You’ll individually help deploy into production use cases with a considerable business impact across various industries. - You’ll work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation. - You’ll work in collaboration with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases our open source codebases for tasks such as inference and fine-tuning. - You’ll be involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. You will provide technical guidance on our products and explain Mistral technologies to various stakeholders. - Your collaboration with our product and science team to improve continuously our product and model capabilities based on customers’ feedback   How We Work in Applied AI   - We care about people and outputs. - What matters is what you ship, not the time you spend on it - Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. - Always ask why. The best solutions come from deep understanding, not from copying what worked before - We say what we mean. Feedback is direct, timely, and given because we care. - No politics. Low ego, high standards. - We embrace an unstructured environment and find joy in it.   About you   - You are fluent in English - You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products - You have proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces. - You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases - You have deep understanding of concepts and algorithms underlying machine learning and LLMs - You have strong technical coding skills in Python - You hold strong communication skills with an ability to explain complex technical concepts in simple terms with technical and non-technical audiences   Ideally you have: - Contributed to open-source projects in particular in the space of LLMs - Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager - You have experience with deep learning with Pytorch Why join us?   You’ll have the opportunity to shape the future of AI adoption in enterprises, work with a world-class team, and contribute to open-source projects that impact millions. If you’re excited about leading technical innovation and solving real-world challenges with AI, we’d love to hear from you! WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: London Workplace: OnSite ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. About The Job   Mistral AI is seeking a Applied AI Engineer to facilitate the adoption of its products among customers and collaborate with them to address complex technical challenges.   The Applied AI team is Mistral's customer-facing technical organization. We work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact.   Our team combines deep ML expertise with strong customer engagement skills, operating like startup CTOs who own end-to-end project execution. By joining the team you'll will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral's technological vision.   What you will do   - You’ll individually help deploy into production use cases with a considerable business impact across various industries. - You’ll work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation. - You’ll work in collaboration with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases our open source codebases for tasks such as inference and fine-tuning. - You’ll be involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. You will provide technical guidance on our products and explain Mistral technologies to various stakeholders. - Your collaboration with our product and science team to improve continuously our product and model capabilities based on customers’ feedback   How We Work in Applied AI   - We care about people and outputs. - What matters is what you ship, not the time you spend on it - Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. - Always ask why. The best solutions come from deep understanding, not from copying what worked before - We say what we mean. Feedback is direct, timely, and given because we care. - No politics. Low ego, high standards. - We embrace an unstructured environment and find joy in it.   About you   - You are fluent in English - You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products - You have proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces. - You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases - You have deep understanding of concepts and algorithms underlying machine learning and LLMs - You have strong technical coding skills in Python - You hold strong communication skills with an ability to explain complex technical concepts in simple terms with technical and non-technical audiences   Ideally you have: - Contributed to open-source projects in particular in the space of LLMs - Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager - You have experience with deep learning with Pytorch Why join us?   You’ll have the opportunity to shape the future of AI adoption in enterprises, work with a world-class team, and contribute to open-source projects that impact millions. If you’re excited about leading technical innovation and solving real-world challenges with AI, we’d love to hear from you! 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.