Top Machine Learning Jobs in Paris

Lead European and international research consortia, grants, and public-private partnerships for a TechBio company. Identify funding opportunities, build and negotiate multidisciplinary consortia, write and defend grant proposals, and manage funded projects, budgets, deliverables, and agency reporting. Serve as the link between AI and biology teams, external collaborators, and funding bodies while representing the company’s Virtual Tissue platform at global innovation and scientific events.

Develop, validate, deploy, and maintain machine learning and deep learning algorithms for EEG, cardiopulmonary, and other biosignal data used in medical devices and clinical applications. Responsibilities include data curation, model development, failure analysis, testing, documentation, regulatory-quality processes, production deployment, tooling improvements, stakeholder presentations, and client support.

Lead the development and delivery of electronic warfare and SIGINT systems from concept through field deployment. Responsibilities include managing a multidisciplinary EW engineering team, defining system architectures, coordinating mechanical, electrical, software, GNC, and ML teams, performing trade-off analyses and simulations, overseeing integration and validation, and maintaining requirements traceability, documentation, and testing consistency.

The role involves reinventing voice recognition technologies for real-world conversations, focusing on real-time speech processing techniques.

Develop and manage systematic trading strategies, optimize quantitative portfolios, and collaborate with quant traders and the investment committee.

The Director of Engineering for Mission Intelligence will manage engineering teams, oversee recruitment, develop standardized processes, and contribute to organizational strategy while remaining technically engaged with projects.

Design, implement, and validate automated manufacturing and test systems. Improve OEE and reliability, integrate automation with ERP/MES, build OEE dashboards and predictive maintenance, prototype robotics solutions, standardize architectures, and train technical staff.

As a PhD Investment & Strategy Associate, you'll conduct research and financial analysis focused on AI-driven technology companies, providing insights for investments while collaborating with a talented team to support strategic decision-making.

As a Senior Fullstack Engineer, you will develop a legal intelligence platform, focusing on user experience, reusable components, and ML integration while collaborating closely with product teams.

You will leverage your technical expertise and sales leadership to drive revenue growth, build partnerships, and represent technology to engineering companies, focusing on capturing new business opportunities in Europe.

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 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.

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.

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.

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.

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.

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.

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.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Own a from-scratch rebuild of Nory's mobile app using React Native and TypeScript, ensuring reliability on low-spec devices and poor connectivity. Set architecture, design system, and migration plan, ship an MVP, integrate AI agents to speed development, and grow the codebase into a platform for cross-functional teams while maintaining high UX and reliability standards.