Top Machine Learning Jobs in Paris

Six-month AI/ML engineering internship focused on building remit, an agentic data science system that transforms business questions and datasets into trusted recommendations. Responsibilities include designing AI agents, implementing data-quality and leakage checks, evaluating agent reliability and cost, integrating modeling tools and tabular foundation models, and supporting Databricks, Unity Catalog, and MLflow integration. The intern will conduct research, benchmark realistic business cases, collaborate with data scientists, and help ensure safe, governed, explainable AI workflows.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.

Lead research on interactive avatar models and real-time generative AI video agents. Set research direction, advance multimodal perception and reaction generation, post-train diffusion models, build evaluation frameworks, define datasets, and guide research-to-production efforts. Mentor researchers and influence technical decisions across research, data, and engineering teams.

The Sales Account Manager will drive revenues from new accounts, develop partnerships, manage forecasting, and collaborate with teams to close deals.

Build, train and deploy large time-series foundation models (multimodal inputs), design reproducible experiments, create data exploration tools, add datasets, deploy models to API/platform, iterate on user feedback, and mentor future team members.

Develop and productize deep learning and computer vision models for autonomous drone navigation. Responsibilities include data curation, model architecture, edge deployment optimization, MLOps pipeline management, reproducible training and evaluation, cross-team localization pipeline development, and real-world flight testing. The role requires researching advanced deep learning methods, optimizing model speed and accuracy on constrained hardware, and analyzing field failures to improve deployed systems.

Drives enterprise sales growth for legal and compliance software products through account planning, new customer acquisition, upselling, cross-selling, and strategic relationship management. Leads complex customer engagements, presents tailored solutions, manages sales quotas and forecasts, and collaborates with solution consultants, marketing, professional services, partnerships, and customer success teams. Requires consultative selling, enterprise account management, CRM proficiency, and strong negotiation and presentation skills.

Provides technical leadership for designing, developing, and deploying AI, machine learning, generative AI, and advanced analytics products. Leads solutions from experimentation through production while promoting software engineering, MLOps, testing, governance, responsible AI, and lifecycle best practices. Collaborates with business and product stakeholders, mentors data scientists and machine learning engineers, conducts technical reviews, and ensures scalable, reliable, interpretable, and maintainable AI solutions.

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.

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.

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.

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.

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.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

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.

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.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

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.