The posting
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.
ABOUT THE JOB
As a Forward Software Engineer, Data, you will build the systems behind some of the hardest problems Mistral takes on.
You join our team of engineers in Applied AI. We gravitate towards hard problems, decide how to solve them, and ship fast. You work closely with our customer-facing teams, and what works goes into production with our customers and across the company. When a project needs it, you work directly on customer engagements.
We're looking for a strong software engineer who builds end to end and has shipped ML or GenAI systems to production. You've felt the pain of models that pass every eval and fail on real data, and of quality that drifts after launch. You've also felt the data engineering pain: pipelines that break silently, schemas that change under you, and backfills that take a weekend. Data engineering is your edge. You turn messy, large-scale data into systems that hold up.
You think like a founder. You start from the customer's problem, not the technology. You measure success by the value you deliver, and you care about building experiences people love to use. You like working at the edge of what AI can do today, and you push past it.
Your scope is end to end, from first prototype to production. Your depth in data architecture helps set the standard for how the team designs and delivers systems. You know lakehouse and data mesh patterns well enough to know when they fit and when they're overkill. You've designed ontologies and data models, and you know that getting them right is as much art as science.
WHAT YOU WILL DO
- Build AI systems end to end, from first prototype to production.
- Design the data architecture underneath them: ingestion, processing, storage, and the data models and ontologies that make messy data usable.
- Build distributed pipelines that process large volumes of documents, code, logs, and multimodal content for retrieval, agentic workflows, and fine-tuning.
- Turn what works for one customer into patterns and platforms that work across many, without rebuilding each time.
- Make systems hold up under real load: throughput, latency, cost, and failure recovery are part of the design from the start.
- Build data governance into the systems themselves: ownership, access control, lineage, retention, and privacy for sensitive enterprise data.
- Build the evaluation loop: labeling with LLMs and humans, versioned eval datasets, and checks that catch quality drift before customers do.
- Set the standard for how the team designs and ships data systems.
- Work with researchers, engineers, and customer-facing teams to turn hard problems into systems people rely on.
WHAT WE'RE LOOKING FOR
- You have full ability to ship across the stack: proficient NodeJS e.g. NextJS/Express/Fastify or similar or Python e.g. FastAPI. You are confident with TypeScript + React/NextJS or similar. Solid beats polished.
- You have 5+ years of experience building and running production data-intensive pipelines with distributed processing (for example Spark, Ray, Beam, or Dask) and orchestration tools (for example Airflow, Dagster, or Prefect) with structured and unstructured data.
- You have experience with retrieval-augmented generation, agentic use cases, or LLM evaluation, and a clear view of how data quality drives model quality.
- You work autonomously: you find the problem, decide the approach, and own the outcome.
- You communicate clearly with technical and non-technical audiences, and are fluent in English.
- You ship in increments, are obsessed with time to value and are comfortable making and articulating product-technical tradeoffs.
IDEALLY YOU HAVE
- A passion for graph-based systems (e.g. Neo4j) and ontology design
- Hands-on experience building data pipelines for LLM training, fine-tuning, or evaluation, and contributions to open-source projects, particularly in data or LLMs.
- Hands-on experience with data governance and privacy requirements (for example GDPR), secure data handling, CI/CD and infrastructure as code, or an affinity for security engineering.
HOW WE WORK IN APPLIED AI
- We care about people and outputs.
- What matters is what you ship, not the time you spend on it
- Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week.
- Always ask why. The best solutions come from deep understanding, not from copying what worked before
- We say what we mean. Feedback is direct, timely, and given because we care.
- No politics. Low ego, high standards.
- We embrace an unstructured environment and find joy in it.
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.
