Applied Scientist - Research Engineer (Optimisation), EMEA

The Role

Mistral AI is seeking Applied Scientists and Research Engineers focused on optimisation and operational research. You will post-train SOTA large language models to formulate and solve optimisation problems, and apply them to diverse use cases.

Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions. We are made of researchers, engineers, product builders, ex-founders who love tackling the most complex real world problems in the industry.

What you will do

• Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs.

• Generate and curate datasets for optimisation modelling (LP, MILP, constraint programming, scheduling, routing), and build evaluators and benchmarks for operational research tasks.

• Use solvers as verifiers to build reward signals for reinforcement learning.

• Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment.

• Collaborate with cross-functional teams to tackle complex use cases.

• Manage research projects and communications with client research teams.

• Fluent in English with excellent communication skills — at ease explaining complex technical concepts to both technical and non-technical audiences.

• Expert with PyTorch or JAX.

• Experience training or fine-tuning large language models for reasoning, code, or mathematics.

• Strong background in operational research or mathematical optimisation, with hands-on experience using solvers (e.g. Gurobi, CPLEX, OR-Tools) and modelling languages (e.g. Pyomo, AMPL).

• Not afraid of contributing to a big codebase; finds their way around independently with little guidance.

• Writes clean, readable, high-performance, fault-tolerant Python code.

• Doesn’t need roadmaps: moves independently and navigates uncertainty with confidence.

• Low-ego, collaborative and eager to learn.

• Track record of success through personal projects, professional projects or academia.

Nice to have

• Ex-founder or builder

• PhD / Master in a relevant field (Operational Research, Applied Mathematics, Machine Learning) — exceptional candidates from other backgrounds welcome.

• Experience with reinforcement learning using verifiable rewards.

• Experience with LLM-based optimisation modelling (e.g. NL4Opt-style problem formulation).

• Industry experience in supply chain, logistics, energy, or scheduling.

• Experience working on large code bases or large machine learning models at scale.

• Contributions to a large open-source or industry codebase.

• Publications in top academic journals or conferences.

• Enjoys improving existing code: typing, tests, CI pipelines.