Junior / Intermediate Machine Learning Engineer - Squad Distribute

Context

We are looking for a Junior / Intermediate Machine Learning Engineer to help us build the first legal AI platform.

Join a squad dedicated to revolutionizing how legal professionals advise clients and businesses across France and Europe. A high-impact product squad responsible for secondary legal sources and private documents. These are complex challenges where you will also contribute to embedding GenAI at the core of our products.

You can find full details about our stack on GitHub https://github.com/DoctrineLegal/#our-tech-stack !

Note: Previous professional experience in law is not required, but a willingness to learn and develop expertise in understanding legal documents is important

Missions

• Develop NLP models (whether based on Machine Learning or not) to assist with information search and recommendation within legal documents, in particular.
• Maintain and monitor these models in production.
• Design conversational agents to accelerate lawyers’ work across various areas of law, ensuring accurate responses and facilitating drafting tasks.
• Collaborate with different types of profiles, including engineers, product managers, and product designers, to identify new product development opportunities.
• Participate in internal knowledge sharing and consolidation of best practices; contribute to the development of our Engineering strategy.

Ideal Profile

• knowledge of Machine Learning (both classical and deep learning) for data extraction and recommendation, ideally with some first-hand experience with LLMs.
• Solid understanding of MLOps challenges, with a strong motivation to grow in this area.
• A keen interest in artificial intelligence topics.
• Desire to identify, analyze, and deploy the latest scientific advances in production.
• Strong Python development skills, with a focus on algorithm design.
• Proficiency in French, as you will be working with legal data in French.

Extras of the role

• Like all engineers at Doctrine, you will join one of our cross-functional chapters - in this case, the Machine Learning chapter. Within this chapter, you will contribute to internal projects aimed at improving our processes and shaping our long-term vision. The chapter meets every week to:
• Share knowledge: keep track of the state of the art, review scientific papers, continuous improvement, best practices, etc.
• Propose improvements: experiment with new tools, suggest new processes to implement.
• You will also take part in recruitment: all individual contributors meet candidates through technical assessments or interviews.