Top Machine Learning Jobs in Paris

As an AI Product Manager, you will lead AI product development, collaborate with teams, conduct market research, and ensure alignment with user needs.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

The Senior Machine Learning Engineer will design and implement AI and ML solutions, build retrieval pipelines, and develop language models to enhance patient care access and management.

Lead global adoption and optimization of the Matrix MMM tool, advise markets on A&P decisions, drive media planning/buying improvements, prioritize product features, enable local performance experts, and consolidate global media planning best practices to improve ROI.

Lead data analytics for a portfolio of mobile games, translating player, user acquisition, and monetization data into actionable insights. Define the analytics roadmap, ensure data quality, automate dashboards, and collaborate with data engineering teams. Manage and mentor analysts, support stakeholders, promote data-driven decision-making, and train teams on data tools and insight interpretation. The role requires SQL, Python, visualization tools, gaming analytics experience, and English and French fluency.

Customer-facing pre-sales role for the French market: present Snowflake to technical and executive audiences, run demos/proofs-of-concept, design solutions, build technical champions, collaborate with sales/partners/product/engineering, and represent Snowflake at events.

Develop and deploy machine learning models for dynamic pricing, fleet repositioning, and marketplace optimization. Build MLOps processes, experimentation infrastructure, data pipelines, KPIs, and real-time monitoring dashboards. Collaborate with international, multidisciplinary teams to influence product strategy and business decisions. The role requires production-quality Python, SQL, BI tools, statistical expertise, and the ability to communicate insights to nontechnical stakeholders.

Work with multidisciplinary teams to integrate and analyze large heterogeneous scientific datasets, develop data processing pipelines, build and deploy ML and predictive models, apply AI and mathematical/systems biology models to support in silico drug design, molecular selection and optimization, and contribute data-driven insights to translational research.

Conduct research advancing self-improving AI, agentic intelligence, autonomous execution, long-horizon reasoning, learning from experience, model architectures, training, and evaluation. Develop novel algorithms, research prototypes, and experimental systems, collaborating with engineers and researchers to turn frontier research into robust, scalable AI systems.

Comand AI is accepting spontaneous applications for future engineering, product, business development, and deployment roles. Potential work includes forward-deployed engineering, computer vision, applied machine learning research, full-stack and backend development, geospatial data, ML/LLM workflows, and military deployment strategy. Strong candidates operate autonomously, handle ambiguity, scope problems independently, and prioritize user outcomes. The company operates from Paris and London with a hybrid setup.

Build and ship end-to-end features for Prevail, an operational planning platform used in live defense deployments. Responsibilities include full-stack or backend development with Python, FastAPI, and TypeScript; geospatial data work using tile servers and PostGIS; resilient ML and LLM workflow orchestration with LangGraph and LangChain; and on-premise appliance deployment. The role involves working independently amid ambiguity, collaborating with leadership, engaging directly with users, and traveling to client sites when deployments require it.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Develop RF signal-processing algorithms for detecting, characterizing, classifying, and identifying emitters across radar, radio, and electronic-warfare systems. Build machine-learning analytics, electronic order-of-battle capabilities, cross-module data-fusion pipelines, and real-time implementations. Prototype in Python or MATLAB, collaborate with software and FPGA engineers, and validate performance using simulated and real-world datasets in laboratory and field environments.

Design and implement resilient navigation and PNT solutions for autonomous platforms operating in contested and GNSS-denied environments. Develop anti-jam and anti-spoof capabilities, multi-sensor fusion, estimation and filtering algorithms, and real-time implementations. Lead integration, prototyping, field validation, architecture reviews, requirements traceability, and technical coordination across hardware, software, EW, GNC, and machine learning teams.

Designs and deploys enterprise AI-ready data platforms, data lakes, lakehouses, warehouses, pipelines, integrations, and sovereign data spaces. Works directly with customers to assess data ecosystems, identify AI opportunities, define transformation roadmaps, and deliver production-ready solutions and proofs of value. Requires strong expertise in distributed data systems, AI architectures, interoperability standards, cloud technologies, automation, CI/CD, DataOps/MLOps, security, governance, and consulting.

Lead end-to-end data and AI architecture for client engagements across cloud infrastructure, data pipelines, machine learning, generative AI, and autonomous agents. Serve as the primary technical contact for senior stakeholders, oversee architecture governance and production readiness, remove engineering impediments, and guide projects from assessment through deployment. Support pre-sales through technical scoping, proposals, estimations, and client presentations while mentoring consultants and engineers and developing internal AI architecture assets.

Design and develop bulk and single-cell multi-omics sequencing pipelines, lead omics acquisition strategy, support drug discovery projects, help design biological experiments, and contribute to academic publications and conferences. The role requires strong R, Python, and Bash programming, software engineering practices, and bioinformatics expertise, with opportunities to work on machine learning-based digital twin technology.

Lead and grow a team of Applied Scientists to research, prototype, and productionize AI/ML capabilities across Product Analytics (event understanding, conversational analytics, automated insight reporting, session-replay issue detection). Define technical vision and roadmap, stay hands-on technically, hire and onboard team members, and coordinate delivery with product and engineering stakeholders.

As a Technical Account Manager, you'll drive customer adoption of AI tools, manage projects, deliver demos, and advocate for user feedback while collaborating across teams to ensure customer success.

Lead Workday Compensation implementations and HCM transformations: run workshops, gather requirements, configure, test, deploy, lead workstreams, liaise with clients and teams, and apply Accenture/Workday methodologies and AI-enabled tools.

Part-time research internship (Alpic + MICS) studying security of LLM + MCP server systems. Investigate attack vectors, develop and train a guardrail classifier to scan MCP inputs/outputs, and train models on Alpic production data. Potential to convert to full-time.

Serve as a pre-sales applied AI architect partnering with GSIs and cloud providers (AWS/GCP) to develop joint GenAI solutions, codify reference architectures, support strategic customer deals, enable partner developer communities, and provide product feedback to inform roadmap and partner strategy.

Design and deploy large-scale ML solutions for adtech: research CTR prediction and online learning, translate papers into production, run A/B tests, monitor live model performance, and collaborate with engineering teams to deliver high-throughput, low-latency prediction systems.

Lead strategic selling of enterprise AI applications and use cases to Global 1000 customers. Develop account engagement plans, drive business development, collaborate with product and engineering, and expand customer footprints. Serve as a technical-sales leader, translating complex AI/ML concepts into business value. Travel to customer sites up to 50%.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.