Legal Research - Internship
Job description
About the role
Filevine builds a part-time internship for law students to validate legal data used by AI systems. The intern reads judicial opinions and verifies machine learning outputs to support proactive legal operations. This role connects legal research training with practical data validation inside a unified legal platform. You will act as a meticulous reviewer who ensures that automated outputs align with the source legal authorities. The position requires you to bridge the gap between technical systems and legal accuracy through structured analysis. You will contribute to protecting the integrity of insights that legal professionals rely on every day. This internship provides a direct line between academic legal training and the practical demands of modern legal technology.
Key facts
What you'll do
Analysis of case law strengthens context available to legal operations teams through structured validation. AI system outputs receive accuracy verification against original legal authorities by trained reviewers. Source legal materials function as the benchmark for validating automated extraction results. Errors, ambiguities, and edge cases within automated data outputs are surfaced through legal research training. Review practices protect the integrity of insights used by legal professionals. Information flows across a cross-functional, international team that refines legal operations handling. You will examine statutory texts and regulatory materials to confirm alignment with automated interpretations. Your work will identify inconsistencies between raw data outputs and underlying judicial reasoning. You will document findings in a clear manner that supports downstream improvements in data pipelines.
Requirements
The posting states a pay range of $20 to $20.
Mandatory enrollment as a 2L or 3L at an accredited US law school is required. Completion of at least the 1L curriculum, including legal research and writing coursework, must be confirmed. Enjoyment of reading and analyzing judicial opinions, statutes, and other primary legal authorities is necessary. Strong attention to detail and familiarity with legal writing conventions are essential, with interest in legal technology or artificial intelligence. You must be currently enrolled and in good academic standing at an accredited institution. Demonstrated ability to conduct independent legal research is a fundamental expectation for this role. Comfort with dense textual material and complex procedural frameworks is required. A commitment to accuracy over speed is emphasized as a core standard for successful performance.
Practical notes
Employment eligibility is limited to current law students, with flexibility for remote or in-office work nationwide. Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
About the role
Filevine builds a part-time internship for law students to validate legal data used by AI systems. The intern reads judicial opinions and verifies machine learning outputs to support proactive legal operations. This role connects legal research training with practical data validation inside a unified legal platform. You will act as a meticulous reviewer who ensures that automated outputs align with the source legal authorities. The position requires you to bridge the gap between technical systems and legal accuracy through structured analysis. You will contribute to protecting the integrity of insights that legal professionals rely on every day. This internship provides a direct line between academic legal training and the practical demands of modern legal technology.
Key facts
What you'll do
Analysis of case law strengthens context available to legal operations teams through structured validation. AI system outputs receive accuracy verification against original legal authorities by trained reviewers. Source legal materials function as the benchmark for validating automated extraction results. Errors, ambiguities, and edge cases within automated data outputs are surfaced through legal research training. Review practices protect the integrity of insights used by legal professionals. Information flows across a cross-functional, international team that refines legal operations handling. You will examine statutory texts and regulatory materials to confirm alignment with automated interpretations. Your work will identify inconsistencies between raw data outputs and underlying judicial reasoning. You will document findings in a clear manner that supports downstream improvements in data pipelines.
Requirements
The posting states a pay range of $20 to $20.
Mandatory enrollment as a 2L or 3L at an accredited US law school is required. Completion of at least the 1L curriculum, including legal research and writing coursework, must be confirmed. Enjoyment of reading and analyzing judicial opinions, statutes, and other primary legal authorities is necessary. Strong attention to detail and familiarity with legal writing conventions are essential, with interest in legal technology or artificial intelligence. You must be currently enrolled and in good academic standing at an accredited institution. Demonstrated ability to conduct independent legal research is a fundamental expectation for this role. Comfort with dense textual material and complex procedural frameworks is required. A commitment to accuracy over speed is emphasized as a core standard for successful performance.
Practical notes
Employment eligibility is limited to current law students, with flexibility for remote or in-office work nationwide. Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
AI systems learn from published legal materials to support legal operations. Legal research and machine learning intersect in modern law practice. Cross-functional teams work on data quality and automation in legal workflows. Part-time schedules accommodate academic calendars for law students. Reading judicial opinions is central to validating automated legal analysis.
Questions to ask
Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.