£110 Per hour
Undetermined
Remote
EMEA
Summary: The Senior Legal Professional role at Pareto involves collaborating with a leading AI lab to develop high-fidelity computer-use exercises that reflect real Legal Document Classification workflows. Candidates will be responsible for authoring realistic tasks, documenting workflows, and creating authentic deliverables while applying quality standards and iterating based on feedback. The position requires significant experience in Legal Document Classification and strong technical aptitude. This role offers remote work flexibility and a competitive hourly compensation rate.
Key Responsibilities:
- Produce 3-5 end-to-end Legal Document Classification scenarios weekly that mirror actual professional work
- Capture starting context, on-screen actions, tool usage, time estimates, and alternative approaches
- Provide real artifacts (reports, configs, analyses, documentation) as task outputs
- Self-grade work using provided rubrics and identify common failure patterns
- Rapidly incorporate QA insights to enhance clarity and coverage
Key Skills:
- 5+ years of professional experience in Legal Document Classification or closely related field
- English fluency (native or near-native proficiency required)
- Technical aptitude strongly preferred—STEM degree, data science background, or demonstrated skills in SQL, scripting, spreadsheets, or version control
- Exceptional written communication—ability to transform implicit expertise into explicit, reproducible instructions
- Tool proficiency with standard Legal Document Classification software and platforms
Salary (Rate): £80–£110 hourly
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: Legal
Senior Legal Professional
Pareto is hiring experienced Senior Legal Legal Professional professionals to collaborate with a leading AI lab on a groundbreaking project. The focus is on creating high-fidelity computer-use exercises that capture real Legal Document Classification workflows—complete with starting context, step-by-step actions, decision points, and final deliverables.
KEY RESPONSIBILITIES
- Author realistic tasks: Produce 3-5 end-to-end Legal Document Classification scenarios weekly that mirror actual professional work
- Document comprehensive workflows: Capture starting context, on-screen actions, tool usage, time estimates, and alternative approaches
- Create authentic deliverables: Provide real artifacts (reports, configs, analyses, documentation) as task outputs
- Apply quality standards: Self-grade work using provided rubrics and identify common failure patterns
- Iterate based on feedback: Rapidly incorporate QA insights to enhance clarity and coverage
IDEAL QUALIFICATIONS
- 5+ years of professional experience in Legal Document Classification or closely related field
- English fluency (native or near-native proficiency required)
- Technical aptitude strongly preferred—STEM degree, data science background, or demonstrated skills in SQL, scripting, spreadsheets, or version control
- Exceptional written communication—ability to transform implicit expertise into explicit, reproducible instructions
- Tool proficiency with standard Legal Document Classification software and platforms—please specify these in your application
PROJECT TIMELINE
- Initial calibration: Paid 1-hour assessment within 3 days of acceptance
- Pilot phase: 1-week paid pilot to demonstrate capabilities
- Extended engagement: Successful pilots lead to 2-month contracts with potential for extension or full-time opportunities
- Time commitment: 10-20 hours per week during pilot and contract phases
COMPENSATION & CONTRACT
- Hourly rate: $80–$110/hr
- Employment type: At-will contractor to Pareto
- Geographic flexibility: Remote work from anywhere; compensation adjusted by location while maintaining competitiveness
- Visa requirements: We cannot currently support H1-B or STEM OPT status candidates
APPLICATION & ONBOARDING PROCESS
For immediate consideration, please complete our interest survey: