Applied Scientist - Optimisation, EMEA (AI)

Location: Paris, France; other locations: Amsterdam, London, and Lausanne

Company

Mistral AI provides full-stack AI solutions, including frontier models, developer tools, applications, and compute, for customized systems across high-stakes industries.

What you will do

• Run pre-training, post-training, and deployment of state-of-the-art models on clusters with thousands of GPUs.
• Generate and curate optimisation datasets covering LP, MILP, constraint programming, scheduling, and routing.
• Build evaluators and benchmarks for operational research tasks.
• Use solvers as verifiers to create reward signals for reinforcement learning.
• Develop tools and frameworks for data generation, model training, evaluation, and deployment.
• Collaborate with science, engineering, product, and client research teams while managing research projects and communications.

Requirements

• Fluent English with excellent communication skills and the ability to explain complex technical concepts to technical and non-technical audiences.
• Expertise 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 such as Gurobi, CPLEX, or OR-Tools and modelling languages such as Pyomo or AMPL.
• Ability to contribute independently to a large codebase and write clean, readable, high-performance, fault-tolerant Python.
• Ability to work independently through uncertainty, collaborate with low ego, and learn actively.
• A track record of success through personal projects, professional projects, or academia.

Nice to have

• Experience as an ex-founder or builder.
• PhD or Master’s degree in operational research, applied mathematics, machine learning, or a related field; exceptional candidates from other backgrounds are welcome.
• Experience with reinforcement learning using verifiable rewards or LLM-based optimisation modelling, such as NL4Opt-style problem formulation.
• Industry experience in supply chain, logistics, energy, or scheduling.
• Experience with large codebases or large machine learning models at scale.
• Contributions to a large open-source or industry codebase.
• Publications in top academic journals or conferences.
• Experience improving existing code through typing, tests, or CI pipelines.

Culture & Benefits

• Collaborative, creative, low-ego, team-oriented environment focused on applied AI innovation.
• Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, and meal and transportation allowances.
• Full-time employment with location-specific benefits available in the selected work location.
• Teams are distributed across Europe, North America, Asia, and the Middle East.