Machine Learning Engineer (Paris/London)

About Mistral 

- At Mistral AI, we are a tight-knit, nimble team dedicated to bringing our cutting-edge AI technology to the world. Our mission is to make AI ubiquitous and open 

- We are creative, low-ego, team-spirited, and have been passionate about AI for years

- We hire people who thrive in competitive environments, because they find them more fun to work in. We hire passionate women and men from all over the world

- Our teams are distributed between France, UK and USA 

Role Summary 

- You will be in charge of deploying state-of-the-art models in production environments, helping turn research breakthroughs into tangible solutions

- Location: Paris / London

Key Responsibilities 

- Create and maintain tooling and services: both internal facing (research & dogfooding) and external facing (product)

- Collaborate cross-functionally with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions

- Implement and optimize ML pipelines for performance and accuracy, ensuring production readiness and employing cutting-edge technology and innovative approaches

About the ML Engineering team 

- Our ML Engineering team is embedded in our Product development organization (SWE & Product) team and works very closely with our Science team 

- All our engineers can fluidly move on the production / research spectrum depending on where the needs are or where their interests lie

Qualifications & profile 

- Master's degree in Computer Science, Machine Learning, Data Science, or a related field

- Expert programming skills in PythonMLOps or FullStack + ML experience

- Proficiency in frameworks like PyTorch or TensorFlow

- Adaptable, proactive and autonomous

- Attention to detail and a drive to go the last mile to build almost perfect tools 

- Deep understanding of machine learning approaches and algorithms

- Low-ego 

- Collaborative and have a real team player mindset

Now, it would be ideal if you have;

- Experience with training and fine-tuning large language models (e.g., distillation, supervised fine-tuning, policy optimization)

- Worked with LLMs

- Worked with research teams before