Model Behavior Architect at Mistral AI designing evaluations and improving function calling in LLMs. Focus on tool use, agentic workflows, and model behavior optimization through eval pipelines and synthetic testing.
As a Model Behavior Architect on the Function Calling team, you are at the forefront of defining and measuring how LLMs use tools, invoke functions, and orchestrate complex agentic workflows.
We are looking for people who have built a career in engineering, machine learning, and large language models and are experts in model evaluation, policy writing, and creating eval pipelines for tool use and function calling. Your role is to work hand-in-hand with our Science team to define what 'good' looks like for function calling—from accurate parameter selection and schema adherence to multi-step tool orchestration, error recovery, and agentic reasoning.
Join us if you are passionate about tackling cutting-edge, open-ended research challenges and transforming your insights into best-in-class models.
Interact with models to identify where function calling and tool use behaviour can be improved
Gather internal and external feedback on tool-calling behaviour to scope areas for improvement
Design and implement evals, data guidelines, data generation, and synthetic tool environments and APIs
Identify and fix edge case behaviours, such as malformed arguments, hallucinated functions, and incorrect tool selection—through rigorous testing
Develop robust evaluation pipelines for the function-calling capabilities of our model candidates
Work collaboratively with AI Scientists
You have a deep understanding of either 1) API design, structured outputs, and schema specification (e.g. JSON Schema), 2) engineering and code behavior, 3) LLM agents at work, including reasoning, planning, and multi-step tool use
You have prior knowledge in training and optimising model behaviour
You are an expert at building robust evaluations
You thrive in dynamic and technically complex environments
You have a track record of delivering innovative, out-of-the-box solutions to address real-world constraints