Research Engineer - Search and Services

About this role

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. Role Summary We are seeking a talented and experienced Research Engineer to join our newly forming Search and Services Platform team within Research Platform. You will build and operate the large-scale search and services that our models rely on as tools — from self-hosted, web-scale, search indexes to knowledge graphs and databases served through MCP.

You will work closely with researchers and with the pretraining data, sandbox and data platform teams to make these services reliable, scalable and fast, on infrastructure measured in hundreds of terabytes of NVMe storage and thousands of CPU cores to support both data generation and critical training runs. What you will do As a Research Engineer on the Search and Services team, your responsibilities will include (but may not be limited to): Designing, building and operating large-scale search infrastructure: self-hosted Vespa clusters indexing web-scale corpora, managed indexes for smaller corpora, and vendor search endpoints.

Self-hosting and maintaining a wide range of structured data sources — knowledge graphs (Wikidata, UniProt, Materials Project), scholarly data (OpenAlex, OpenAIRE), statistical databases (Eurostat, World Bank, IMF, OECD), open data portals and more. Designing interfaces and tools to expose to the models and agents to use the services in different ways (direct YQL or SPARQL queries, or more refined and constrained MCPs to mimic external services) to support data generation and agentic training.