AI Scientist (Agentic Engineering)

Location: Remote, with listed locations in Paris, Linz, Munich, London, and Berlin.

Company

Mistral AI develops full-stack AI solutions spanning frontier models, developer tools, applications, and compute for high-stakes industries.

What you will do

• Design pretraining, supervised fine-tuning, and reinforcement-learning data for engineering tasks across CAD, CAE, semiconductor simulation, and EDA.
• Define verifiers, evaluation criteria, benchmarks, and diagnostic tooling that measure genuine engineering task competence and identify model failures.
• Design and improve agent architectures and harnesses for planning, tool use, error recovery, and multi-step engineering workflows.
• Collaborate with CAE and EDA domain experts to prioritize high-value engineering workflows and translate domain knowledge into training signals.
• Contribute findings and methodology to broader pre- and post-training research across the science organization.

Requirements

• Fluent English with excellent communication skills, including the ability to explain technical ML and engineering concepts to engineering and non-technical audiences.
• Deep, hands-on machine learning expertise, particularly in LLM development.
• Demonstrated experience running, debugging, and validating real engineering workflows in at least one of CAD, CAE, semiconductor simulation, or EDA.
• Clean, readable Python code and comfort working in Linux/HPC environments.
• Self-directed approach and ability to make progress without detailed roadmaps.
• Low-ego, collaborative mindset and eagerness to learn at the intersection of engineering and ML.

Nice to have

• Experience building or fine-tuning agentic systems, including tool use, multi-step planning, and agent orchestration frameworks.
• Experience with reward modeling, RLHF, RLAIF, RLVR, or preference-based training.
• Industrial or academic experience with CAE or EDA tools such as SolidWorks, CATIA, Fluent, Abaqus, LS-DYNA, STAR-CCM+, or Cadence, Synopsys, and Siemens EDA tools.
• Contributions to a large open-source or industry codebase.
• Publications in engineering and ML venues such as NeurIPS, ICLR, JFM, or AIAA.

Culture & Benefits

• Full-time role within the Science / AI for Science department.
• Collaborative, low-ego, team-oriented environment with distributed teams across Europe, North America, Asia, and the Middle East.
• Benefits may vary by country and include healthcare, parental leave, retirement plans, relocation support, wellness programs, and meal and transportation allowances.