Applied AI, Evaluation Engineer
As the first Evaluation Engineer in the Applied AI team at Mistral AI, you will be responsible for designing and implementing comprehensive evaluation frameworks to measure LLM capabilities across diverse customer use cases. You will build scalable evaluation infrastructure, develop novel methodologies, and create custom evaluation suites tailored to enterprise needs, working at the intersection of research, engineering, and solutions.
At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life. We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work. 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 France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited. Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact.
The Applied AI team is Mistral's customer-facing technical organization. We work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact. Our team combines deep ML expertise with strong customer engagement skills, operating like startup CTOs who own end-to-end project execution.
However, the AI graveyard is full of great ideas nobody could measure or prototypes that never made it to production. As a first Evaluation Engineer, you'll design the methodology, build the infrastructure, and define what "ready for production" means across verticals and use cases.
You will design and implement evaluation systems that help our customers understand model performance across their specific use cases, build robust evaluation infrastructure, and work closely with both research and customer-facing teams.
Research builds evals for frontier capabilities but customers don't care about MMLU scores. We need in Applied AI evals and frameworks for customer reality domain-specific, risk-aware, production-grade. The kind that tell you whether your medical summarization model will hallucinate drug interactions, or whether your legal assistant will invent case citations.
This role sits at the intersection of research, engineering, and solutions, you will play a critical cross role in measuring, understanding, and improving the capabilities of our models for our enterprise customers.
Posted June 9, 2026