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Doctrine

Posted 4 months ago

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Experienced/Senior Machine Learning Engineer - Squad Maât

ParisHybridFull-time

AI Summary

Experienced/Senior Machine Learning Engineer building NLP models and conversational agents for legal documents. Develops, maintains, and monitors ML models in production, collaborates across engineering and product teams, and contributes to engineering strategy.

About this role

Our Mission ⚖️

Since 2016, Doctrine has been committed to a major democratic challenge: making the law more accessible. We are the first AI-powered legal platform that helps lawyers and legal professionals free themselves from repetitive, time-consuming tasks so they can focus on what truly matters—at every stage of their work: whether it’s understanding a case, conducting legal research, or drafting documents.
Today, Doctrine welcomes over 1 million visits every month, with dozens of new legal professionals joining us every single day. Our ambition goes beyond borders: we aim to become the European leader in legal AI. After France, we have already launched Doctrine in Italy, and we are currently expanding into Germany with a strong conviction: you must think globally while executing locally. That’s why we work closely with our clients, striving to understand the unique subtleties of each legal system. And this is just the beginning!

Our Values 🤝

Challenge the status quo. We embrace bold ideas and smart risk-taking.
Liberty and responsibility. We promote autonomy, individual impact, and ownership.
Knowledge is power. Information lies at the heart of Doctrine’s mission, and we always want to learn more.
Release early, release often, and listen to your customers. We believe in the power of iteration and in constantly listening to our market, our clients, and their challenges.

Context

We are looking for a confirmed/senior 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. Following our successful product launch, including a homemade contract analysis tool, an AI-powered chatbot, and a Word add-in. We're now focused on international expansion and developing innovative new features.

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 👀

  • Strong knowledge of Machine Learning (both classical and deep learning) for data extraction and recommendation, ideally with some first-hand experience with LLMs.
  • Experience with natural language search engines and inverted indexes such as Elasticsearch.
  • 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 Web 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.

    • Skills

    ElasticsearchFrenchLLMsMachine LearningMLOpsNLPPython

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