Practice Innovation Counsel

EvenUp EvenUp · Vertical AI · San Francisco, CA · Hybrid · Product Management

This role focuses on building AI-generated legal work product by translating legal expertise into structured requirements for AI systems. It involves developing mastery in practice areas, defining logic for models, and partnering with engineering and data science teams. The role also includes quality assurance and evaluation of AI outputs, with a strong emphasis on working fluency with LLMs and their limitations.

What you'd actually do

  1. Help build the drafting systems behind our AI-generated work product by decomposing how an expert lawyer reasons through a document into explicit decision trees, defining what every conclusion has to be anchored to in the record, and refining that logic against real output until it holds up across cases.
  2. Develop working mastery in unfamiliar practice areas on short timelines, including the governing statutory and regulatory framework, procedural posture, controlling authority, evidentiary standards, and document conventions. Become the internal authority the rest of the company relies on.
  3. Translate legal judgment into product requirements by partnering with Product, Data Science, and Machine Learning teams to convert "what a good lawyer would do here" into specifications, taxonomies, and evaluation criteria that non-lawyers can build against.
  4. Maintain and expand EvenUp's knowledge base and case-specific templates, including firm and state-level customization in partnership with clients, for personal injury claims today and for additional practice areas as we expand into them.
  5. Conduct legal research on liability and damages nationwide, monitor statutory and common law changes, and mentor the Legal Operations Team on evolving legal standards.

Skills

Required

  • Active license to practice law and in good standing with at least one U.S. state bar
  • 5+ years of substantive legal experience in personal injury litigation
  • Deep understanding of statutory and common law frameworks, insurance coverage, and damages
  • Exceptional legal drafting skills and attention to detail
  • Ability to translate legal knowledge into clear direction for non-lawyers
  • Working fluency with large language models

Nice to have

  • AI-native or comfortable with AI development tools
  • Comfortable working in tools like Claude Code or Cursor
  • Comfort in front of clients

What the JD emphasized

  • Active license to practice law and in good standing with at least one U.S. state bar.
  • 5+ years of substantive legal experience, with real depth in personal injury litigation across multiple states: statutory and common law frameworks, first- and third-party bad faith claims involving all types of insurance coverage, and a strong grasp of recoverable damages and their limitations.
  • A track record of developing mastery in more than one practice area. You have moved between substantive areas of law and can point to a time you became the person colleagues came to on an unfamiliar subject in weeks, not years.
  • Exceptional legal drafting skills and meticulous attention to detail across all forms of written communication, with a track record of producing complex documents such as pre- and post-litigation demands, medical summaries, written discovery responses, and legal memoranda.
  • The ability to turn legal knowledge into actionable, unambiguous direction for non-lawyers. You can explain a doctrinal edge case to an engineer or data scientist in terms they can build against, and you can tell the difference between an instruction that sounds clear and one that actually is.
  • Working fluency with large language models. You use them in real substantive work, you have a concrete sense of where they fail, and you have opinions about how to get better output.

Other signals

  • AI-generated work product
  • translating legal judgment into product requirements
  • partnering with Product, Data Science, and Machine Learning teams
  • working fluency with large language models
  • lead quality assurance efforts through audits, benchmarking, and evaluation development to support model evaluation