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.
About you
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.