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Remote | AI Legal Fellow — Litigation & Motion Practice
Remote / Online - Candidates ideally in
San Francisco, San Francisco County, California, 94199, USA
Listed on 2026-10-09
San Francisco, San Francisco County, California, 94199, USA
Listing for:
24-MAG
Part Time, Remote/Work from Home
position Listed on 2026-10-09
Job specializations:
-
Law/Legal
Litigation, AI Evaluation
Job Description & How to Apply Below
We are sharing a specialised part-time consulting opportunity for experienced litigators with substantial federal civil-litigation and motion-practice experience to contribute to an advanced legal AI training and evaluation project focused on persuasive advocacy, briefing, and litigation strategy.
Selected professionals will evaluate AI-generated motions and litigation responses, perform simulated drafting and strategy exercises, and help define rigorous standards for effective legal advocacy. The work is particularly suited to litigators with a demonstrated record of successful federal motion practice and strong judgement around argument structure, use of authority, factual presentation, and persuasive writing.
Key Responsibilities Motion Drafting & Litigation Strategy- Perform simulated motion drafting, briefing, and litigation-strategy exercises
- Develop persuasive arguments grounded in applicable facts, procedure, and authority
- Evaluate how legal issues should be framed for effective judicial advocacy
- Assess factual organisation, argument sequencing, and strategic positioning
- Apply professional litigation judgement across varied federal civil matters
- Review AI-generated motions and responses to litigation scenarios
- Assess legal reasoning, argument quality, precision, and persuasive effectiveness
- Identify weak analysis, unsupported conclusions, or ineffective advocacy choices
- Evaluate whether authority and facts are used appropriately
- Provide detailed expert feedback supporting improvement of AI-generated legal work
- Apply substantial experience with federal civil litigation and motion practice
- Evaluate briefing approaches across different procedural and substantive contexts
- Assess motions to dismiss, summary judgment, discovery motions, and related filings where relevant
- Identify distinctions between technically competent writing and strong persuasive advocacy
- Apply practical judgement informed by real-world courtroom and briefing experience
- Develop objective evaluation frameworks for litigation-related tasks
- Define grading criteria covering legal analysis, advocacy, authority, organisation, and strategy
- Establish consistent standards for assessing AI-generated motions and briefs
- Document why particular responses are strong, incomplete, or ineffective
- Support rigorous and repeatable legal-evaluation workflows
- Collaborate with research, product, and technical teams on litigation-focused evaluation work
- Translate litigation expertise into clear and actionable feedback
- Help refine legal datasets, review guidelines, and evaluation standards
- Communicate complex advocacy and motion-practice concepts across multidisciplinary teams
- Contribute to evolving standards for AI-assisted litigation support
- Substantial experience litigating civil matters in federal court
- Strong background in motion practice, briefing, and litigation strategy
- Demonstrated record of at least 3 motions won in federal civil litigation
- Ability to provide supporting documentation such as court orders or docket entries
- Exceptional legal writing and persuasive-advocacy skills
- Strong analytical judgement and attention to detail
- Ability to distinguish technically correct analysis from highly effective advocacy
- Experience assessing argument structure, use of authority, and factual presentation
- Excellent written and verbal communication skills
- Ability to translate litigation expertise into structured evaluation feedback
- Experience working with cross-disciplinary teams in fast-paced environments
- Exposure to AI, legal technology, or related training initiatives is advantageous
- Experience across multiple motion types is highly…
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