Mistral AI is looking for AI Scientists with deep machine learning expertise and hands-on engineering experience to expand what Mistral's agentic tools can do across the engineering lifecycle across CAE (CFD, FEA etc) and EDA/Semi..

Within AI4Engineering Science, your core work is building the pre and post-training data for Mistral's LLMs to reason about and execute real engineering tasks. Alongside this, you'll help shape the agent architectures and harness that let these models operate reliably inside multi-step engineering workflows, not just answer isolated questions.

You'll work closely with domain experts across CAE and EDA to ground this work in how engineers actually work, and with the broader research team to translate that domain grounding into training signal and evaluation benchmarks that measure genuine task competence.

What You Will Do

Design pretraining, SFT, and RL data for engineering tasks across CAD, CAE, and semiconductor/EDA
Define verifiers and evaluation criteria that capture what "correct" and "high-quality" actually mean for each engineering task, beyond surface-level plausibility
Design and improve agent architectures and harnesses: how models plan, call tools, recover from errors, and chain steps together across long-horizon engineering workflows
Build evaluation benchmarks and diagnostic tooling to identify where models fail on engineering tasks, and trace those failures back to gaps in data, reward design, or agent scaffolding
Collaborate with domain experts across CAEand EDA (and the science and solutions teams more broadly) to identify which engineering workflows are highest-value to target next
Contribute to Mistral's broader pre and post-training research, sharing findings and methodology across the science organization

Qualifications

Fluent English with excellent communication skills, able to explain technical ML and engineering concepts to both engineering and non-technical audiences
Deep, hands-on machine learning expertise, particularly LLM development
Demonstrated experience running, debugging, and validating real engineering workflows in at least one of CAD, CAE, semiconductor simulation, or EDA.
You write clean, readable Python code and are comfortable in Linux/HPC environments
Self-directed, you don't need detailed roadmaps to make progress
Low-ego, collaborative, and eager to learn at the intersection of engineering and ML

Nice to have

Have experience building or fine-tuning agentic systems (tool use, multi-step planning, agent orchestration frameworks)
Have experience with reward modeling, RLHF/RLAIF/RLVR, or preference-based training
Have industrial or academic experience with CAE, or EDA tools (e.g. SolidWorks, CATIA, Fluent, Abaqus, LS-DYNA, STAR-CCM+, Cadence/Synopsys/Siemens EDA tools)
Have contributed to a large open-source or industry codebase
Have publications in engineering and ML venues (NeurIPS, ICLR, JFM, AIAA, etc.)