Top Machine Learning Jobs in France

Build and maintain large-scale data pipelines, transform raw location and behavioral data into reliable features, and create datasets for products, analytics, and machine learning. Explore complex data, develop metrics and visualizations, monitor quality, investigate anomalies, and translate exploratory analyses into production systems. Collaborate with product, engineering, and data teams to improve data infrastructure and product intelligence.

Coordinate battlefield simulation activities for next-generation rotorcraft programs. Develop mission architectures, operational scenarios, CONOPS, system-of-systems analyses, constructive simulations, and trade studies using MBSE and tools such as Cameo and VR-Forces. Lead testing, validation, documentation, training, and collaboration with military, industrial, NATO, academic, and cross-functional partners. Help establish simulation competencies involving physics modeling, terrain, AI, geospatial data, and software integration.

Build, train, deploy, and monitor production machine learning systems for player safety and security. Develop solutions using NLP, LLMs, anomaly detection, and graph-based learning, adapting research to scalable production environments. Own projects end to end, define success metrics, monitor live systems, optimize code, and implement MLOps practices including CI/CD, model versioning, and observability. Collaborate with data and software engineers to deliver prediction pipelines on cloud infrastructure.

Leads Ipsen’s statistical innovation strategy across R&D, including innovative trial design, simulation, advanced modeling, predictive analytics, digital twins, virtual controls, and data mining. Manages a specialist quantitative team, establishes methodological standards, supports regulatory and portfolio decisions, and partners with biostatistics, clinical development, data science, and other cross-functional groups. Develops talent, oversees reproducible and submission-ready methods, and builds external scientific networks.

Optimize large-scale ML inference systems for throughput, latency, cold-start time, and cost per token. Tune SGLang, vLLM, and TensorRT-LLM across GPUs and distributed clusters; improve batching, scheduling, KV caching, quantization, weight loading, JIT caching, and multi-node inference. Profile and instrument systems to validate performance gains, eliminate storage and networking bottlenecks, and establish technical direction through reproducible benchmarks and experiments.

Six-month software engineering internship developing and testing Java components for Murex’s large-scale financial platform. Responsibilities may include backend services, distributed systems, performance optimization, data processing, monitoring, CI/CD, workflow tools, software architecture, and AI-assisted engineering. Interns analyze existing systems, improve reliability and maintainability, collaborate with experienced engineers, document solutions, and present findings. The role is based in Paris and requires strong Java, object-oriented programming, analytical, communication, and teamwork skills.

Own the architecture, roadmap, tooling, and engineering standards for a machine learning monorepo supporting experimentation through production deployment. Build CI, testing, dependency, container, registry, scheduling, model packaging, and inference infrastructure across GCP and HPC environments. Ensure reproducibility, traceability, performance, and reliability while leading agentic coding adoption. Mentor engineers, review code, collaborate with researchers, and resolve platform issues.

Leads a senior engineering team across backend, frontend, mobile, and machine learning systems. Owns delivery, reliability, incident response, security, compliance, hiring, team development, architecture standards, developer experience, and responsible AI tooling. Partners with product, design, customers, prospects, and executives on roadmap and technical tradeoffs. This remote-first role requires strong technical judgment, engineering management experience, and production systems expertise.

Six-month software engineering internship focused on backend or full-stack development. Interns build high-scale, low-latency systems, web applications, APIs, data pipelines, or data-quality solutions while collaborating with mentors and applying software engineering best practices.

Conduct applied and fundamental AI research in computational advertising, focusing on generative video models, foundation models, generative modeling, bias, and flow matching. Develop scalable, product-ready algorithms, contribute to publications and patents, collaborate with academic partners, and help shape product innovation.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Design and deploy production-scale AI systems, develop and optimize machine learning and neural network models, work with large language models, refine prompt engineering techniques, and use OpenAI and Anthropic APIs. Collaborate with interdisciplinary teams to build AI applications for the finance sector while keeping current with industry developments.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Own and operate globally distributed GPU infrastructure for model training, evaluation, and serving. Responsibilities include running Kubernetes GPU clusters, optimizing scheduling and placement, repairing unhealthy nodes, managing cloud capacity, and debugging Linux, hardware, storage, and networking failures. The role also involves designing GPU clusters, collaborating with research and product teams, and improving compute reliability and utilization.

Own and operate GPU infrastructure for model training, evaluation, and serving. Responsibilities include running Kubernetes GPU clusters, improving scheduling and placement, repairing unhealthy nodes, managing cloud capacity, and debugging Linux, GPU, storage, and networking failures. The role partners with infrastructure, research, product, and cloud-provider teams to build globally distributed compute systems.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Six-month full-time internship to research and implement state-of-the-art computer vision and deep learning models, integrate CV with LLM applications, and collaborate with the ML engineering team to deploy models used by solar and roofing customers.

Lead product development for Datadog APM features, focusing on distributed tracing, performance analysis, and AI-driven troubleshooting. Work with engineers, designers, and customers to validate problems, deliver prototypes to production, define go-to-market, and measure adoption and impact.

Develop motion planning, decision-making, vehicle control, and estimation algorithms for autonomous vehicles. Implement real-time C++ on embedded automotive hardware, build Python and C++ simulation tools, integrate planning with perception, validate software on vehicles, analyze field data, define safety metrics, and improve driving behaviors. The role requires strong software fundamentals, robotics or controls expertise, and experience deploying production-quality systems.

Advise CIOs, CDOs, and AI leaders on data modernization, analytics, and AI adoption. Lead solution design, architecture, proposals, demonstrations, proofs of concept, and technical pre-sales for complex transformation engagements. Translate business goals into scalable data and AI strategies while collaborating with hyperscalers and technology partners. Guide governance, security, compliance, machine learning, generative AI, and advanced analytics initiatives for multinational customers.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Lead end-to-end delivery of complex Industrial AI and digital transformation projects, managing scope, schedules, budgets, risks, financial performance, governance, customer outcomes, adoption, and value realization. Collaborate with technical teams and executive stakeholders, facilitate steering committees and value workshops, support change management and training, and improve delivery practices. The role requires enterprise technology implementation experience, technical fluency, industrial-sector familiarity, strong commercial skills, French fluency, and travel of up to 25%.

Build and improve data infrastructure, data lakes, pipelines, analytical systems, and machine-learning services. Source, store, process, and analyze large datasets reliably and securely; integrate multiple data sources; and deploy scalable, production-ready services using models developed by data scientists. The role also requires learning unfamiliar datasets quickly and collaborating across international teams in ambiguous environments.

Designs, develops, and deploys production-grade machine learning systems focused on LLMs, Generative AI, and deep learning. Responsibilities include building scalable ML libraries and research workflows, optimizing distributed training and inference, improving experiment tracking and model lifecycle management, and bridging research prototypes with robust implementations. The role requires collaboration with data science and platform teams, strong software engineering practices, cloud and MLOps expertise, and mentoring across the technical team. Biomedical, clinical, or omics data experience is preferred.

Drive delivery of core enterprise product features for ENGIE in France. Translate product strategy into functional requirements, user stories, roadmaps, and prioritized backlogs. Partner with engineering, design, and delivery teams through Agile sprint cycles, manage internal and client stakeholders in French and English, and use operational data and platform metrics to refine capabilities. The role also involves complex enterprise integrations, with SaaS, energy-sector, AI, machine learning, and API experience advantageous.