Litigation Attorney
Quick facts
- Type
- Part-time
- Location
- Remote
- Background
- JD / attorney
- Platform
- Micro1 — 260 open jobs
- Typical pay at Micro1
- $25–150/hour · median $90 (17 jobs on our site)
- Apply
- Free to apply — never pay to get AI training work
This role is also open in other regions
pay
Required Skills
Litigation Strategy
Legal Writing
Issue Spotting
Legal Research
Civil Procedure
About micro1
micro1 is the leading AI data lab for training frontier models and evaluating AI agents. Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more. micro1 transforms that real-world expertise into high-quality training data, evaluations, and feedback loops that improve how AI systems learn, reason, and perform.
Our platform identifies and vets top talent through an AI recruiter, enabling high-quality expert contributions at scale. We aim to enable 1 billion people to do meaningful work by applying their expertise to AI. As our global expert network grows, micro1 is building the human intelligence layer for frontier AI.
Job Title: Litigation Attorney
Job Type: Contractor
Location: Remote
Job Summary: We are seeking seasoned U.S.-licensed litigation attorneys for a part-time role at the forefront of legal AI. This opportunity is for accomplished practitioners who want to help shape how advanced AI is trained, evaluated, and applied in real-world legal work. We are especially interested in experienced litigators with deep expertise in legal research, writing, motion practice, and case strategy, as well as attorneys practicing in corporate, real estate, bankruptcy, or compliance.
In this role, you will review, assess, and contribute to litigation-focused workflows used to train and evaluate state-of-the-art AI models. Your work will directly improve how these systems analyze legal issues, construct arguments, and interpret case law and procedure, creating tools with improved precision and legal judgment.
Key Responsibilities
Perform simulated litigation tasks such as issue spotting, legal analysis, and argument construction.
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 legal tasks with rigor and consistency.
Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven legal solutions.
Required Skills and Qualifications:
J.D. from an ABA-accredited law school (
J.D. only
; no LL.M.-only or foreign-equivalent candidates).
Active bar admission in at least one U.S. jurisdiction.
Actively practicing attorney in litigation, corporate, real estate, bankruptcy, or compliance, with a strong preference for seasoned litigators.
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.
Experience working with cross-disciplinary teams in fast-paced environments.
Preferred Qualifications
Judicial clerkship experience (federal or state).
Legal instruction experience, such as teaching or lecturing at a law school.
Law journal or law review editorial experience during law school.
Prior exposure to AI, legal tech, or training initiatives.
Why Join:
This is an opportunity to work at the intersection of law and technology.
You will help define how AI is developed for a new generation of legal practitioners.
You will apply your experience in a high-impact research environment.
Email
By clicking any of the "Continue" buttons below, you acknowledge and agree to our Terms and Privacy Policy for candidates.
Continue
or
Continue with Google
Continue with LinkedIn
Have any questions? See FAQs
Refer and Earn
$1000
About Micro1
micro1 recruits engineers and domain experts for AI labs using its AI interviewer. Once vetted, contractors work on human-data projects such as writing and checking model outputs or building coding evaluations.