AI Legal Fellow — Litigation & Motion Practice
About the role
We are seeking accomplished litigators to join The Winning Motion, a part time AI fellowship focused on advancing the quality of AI generated legal advocacy. The fellowship brings together experienced lawyers with strong motion practice backgrounds to help train and evaluate frontier AI models on one of the hardest remaining problems in legal AI: not simply reaching the right legal answer, but turning sound legal analysis into persuasive advocacy.
What you'll do
- Perform simulated motion drafting, briefing, and litigation strategy exercises.
- Review and assess AI responses to litigation scenarios, providing expert feedback to improve model performance and output precision.
- Create objective evaluation frameworks and grading criteria to assess AI performance on litigation tasks with rigor and consistency.
- Collaborate with product and research teams to refine data, guidelines, and best practices for AI driven litigation support solutions.
Requirements
- Substantial experience litigating civil matters in federal court, including motion practice, briefing, and case strategy.
- A record of at least 3 motions won in federal civil litigation, with supporting documentation (e.g., court orders or docket entries) required.
- Exceptional written and verbal communication skills with meticulous attention to detail.
- Strong analytical capabilities and ability to translate legal expertise into actionable feedback for AI systems.
- Demonstrated commitment to innovation at the intersection of law and technology.
- Experience working with cross disciplinary teams in fast paced environments.
- Preferred: Prior exposure to AI, legal tech, or training initiatives.
- Preferred: Experience across a range of motion types (e.g., motions to dismiss, summary judgment, discovery motions).
About micro1
micro1 is a data lab that helps AI labs and large companies train AI models and agents. It finds and checks skilled people from many fields, like law, medicine, finance and coding, to produce the training data that makes AI work better.
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