AI Jobs in France

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Design rigorous electrical engineering problems reflecting professional practice. Evaluate AI solutions for correctness, assumptions, and constraints. Validate calculations or simulations using Python (NumPy, Pandas, SciPy). Improve AI reasoning to align with industry-standard logic. Apply structured scoring criteria to multi-step problems.

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Design and implement comprehensive evaluation frameworks to measure LLM capabilities across diverse customer use cases including text generation, reasoning, code, and domain-specific applications; build scalable evaluation infrastructure and pipelines that enable rapid, reproducible assessment of model performance; develop novel evaluation methodologies to assess emerging capabilities or verticalized use cases such as cybersecurity, finance, and healthcare; create custom evaluation suites tailored to enterprise customers' specific needs while working closely with them to understand their requirements and success criteria; collaborate with research teams to translate evaluation insights into model improvements and training decisions; partner with product teams to continuously improve evaluation tooling based on customer feedback.

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The Applied AI engineer at Mistral works within the customer-facing technical team to deploy AI solutions that deliver measurable business impact. Responsibilities include identifying high-value internal use cases across various departments such as engineering, legal, HR, sales, and operations; building end-to-end LLM applications including prompts, RAG pipelines, APIs, simple UIs, deployment, and monitoring; owning the full lifecycle of AI tools from prototype to production, maintenance, and iteration; documenting learnings and sharing insights with product and research teams; and converting successful internal tools into customer demos or case studies where appropriate. The role also involves acting as the first internal customer for these tools to identify edge cases and limitations, improving models through usage feedback.

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Develop the front-end of the visual inertial odometry (VIO) algorithmic stack including matching between frames and stereo pairs, calibration of cameras intrinsic and extrinsic parameters, and detection of obstruction. Implement and optimize the algorithmic stack for embedded platforms. Conduct testing, validation, and monitoring of algorithms in simulation and real-world environments, and develop inspection and monitoring tools. Collaborate closely with system engineers, optical engineers, and software engineers, and communicate findings effectively to stakeholders.

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The responsibilities include conducting research on state-of-the-art Computer Vision methodologies and participating in the creation and curation of training and validation datasets. Performing statistical analyses and developing visualization tools to ensure data quality. Building and refining training pipelines and metrics to enhance model performance. Developing and optimizing Computer Vision algorithms for multiple robotics/aerospace projects. Implementing ML/CV models into production-ready environments, ensuring seamless integration with Harmattan AI’s systems, and conducting rigorous code reviews. Testing algorithms in real-world environments, developing monitoring tools, tracking model performance, and continuously improving deployed solutions. Working closely with software and simulation teams to align development with system requirements and communicating findings effectively to stakeholders.

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The Forward Deployed Engineer (FDE) role involves designing and shipping production systems around AI models, owning integrations, data provenance, reliability, and on-call readiness across research, clinical, and operational workflows. The engineer leads discovery and scoping from pre-sales through post-sales, translating ambiguous workflow needs into hypothesis-driven problem framing, system requirements, and execution plans with measurable endpoints. They define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, and outcome metrics, driving delivery until sustained production impact is demonstrated. The role requires building in sensitive scientific data environments with considerations for auditability, validation, and access controls influencing architecture, operating procedures, and failure handling. The engineer runs evaluation loops to measure model and system quality against workflow-specific scientific benchmarks and uses results to drive model and product changes. They distill deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that scale across regulated life sciences environments.

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Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

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The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions addressing the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to their customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to gain visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.