Research Engineer - Eval Platform

Mistral
Paris, France

Who this role is best for

Candidates with experience in distributed systems and a focus on model evaluation will find this Research Engineer role at Mistral highly suitable, especially given the emphasis on reproducibility and collaboration across global teams.

Best fit for

  • Candidates with a background in large-scale ML systems and a passion for ensuring reliable evaluation processes
    — “Build systems that keep eval results reproducible and comparable over time”
  • Individuals who have worked on GPU cluster workloads and understand the nuances of model inference
    — “Experience running workloads on GPU clusters (Slurm, Kubernetes, Ray or similar)”
  • Professionals with a product mindset and experience translating research needs into user-friendly tools
    — “A product mindset: researchers are your users”

Things to consider

  • The role requires a strong commitment to software design and CI/CD practices
    — “Excellent Python and strong software-design instincts: testing, code review, CI/CD”
  • The position assumes a global team dynamic with no explicit remote flexibility mentioned
    — “Our teams are distributed between Europe, North America, Asia and the Middle East”

How to stand out

  • Emphasize your understanding of statistical significance and variance in model evaluation
    — “Statistics for experimentation: variance estimation, significance testing”
  • Mention any contributions to open-source ML tooling or evaluation harnesses
    — “Open-source contributions to ML tooling”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • building reliable, reproducible, and fast evaluation infrastructure
  • running evaluations at scale
Typical background
Master's or PhD in Computer Science4+ years of experience in production-grade software development

Skills & requirements

Required

Production-grade Software DevelopmentLarge-scale ML CodebasesDistributed SystemsGPU ClustersLLM Inference And Evaluation

Preferred

Building Or Maintaining Evaluation HarnessesInference EnginesAgentic Or RL EnvironmentsStatistics For Experimentation

Stack & domain

PythonLLM InferenceEvaluationGPU ClustersDistributed SystemsAgentic ModelsRL EnvironmentsStatisticsProduct MindsetSelf-starterCollaborativeLow-egoAIMLSoftware Development

About the role

Original posting from Mistral via Ashby

ABOUT MISTRAL

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.

We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

THE ROLE

Evaluation is how we decide which models, checkpoints and recipes ship. As a Research Engineer on the Eval Platform team, you will build the infrastructure every science team relies on to measure model quality, and make it reliable, reproducible and fast.

You don't need to have designed benchmarks before. You do need to care about what a score means, and about when a difference between two runs is real.

WHAT YOU WILL DO

  • Build systems that keep eval results reproducible and comparable over time, as models, benchmarks and code evolve.
  • Run evaluations at scale across our GPU clusters, from model serving to scoring.
  • Make eval results easy to access, explore and trust, through APIs and dashboards that researchers use every day.
  • Catch broken or noisy evals before they mislead research decisions.
  • Support evaluation of agentic, multi-turn and tool-using models.
  • Work closely with researchers to turn new evaluation needs into robust, shared tooling.

WHAT WE'RE LOOKING FOR

  • Master's or PhD in Computer Science, or equivalent experience.
  • 4+ years building production-grade software, ideally large-scale ML codebases or distributed systems.
  • Excellent Python and strong software-design instincts: testing, code review, CI/CD.
  • Experience running workloads on GPU clusters (Slurm, Kubernetes, Ray or similar).
  • Familiarity with LLM inference and evaluation.
  • A product mindset: researchers are your users.
  • Self-starter, low-ego, collaborative.

NICE TO HAVE

  • Experience building or maintaining evaluation harnesses or benchmarks.
  • Hands-on experience with inference engines such as vLLM or SGLang.
  • Experience with agentic or RL environments.
  • Statistics for experimentation: variance estimation, significance testing.
  • Open-source contributions to ML tooling.

WHAT WE OFFER

We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.

For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link.

PRIVACY POLICY

Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Source: Mistral careers (Ashby)

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