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M&A Contract Review Attorney, Remote Part-Time

Join a remote, part-time role evaluating AI responses to M&A contracts and shape how legal AI understands redlines, risk, and deal language. Requires a J.D., active U.S. bar admission, and 2+ years in M&A; pay is $80–$105/hr for 20+ hours/week.

OpenTrain AI

Legal Finance

100% Remote Hourly · $80–$105/hr

$80–$105/hr

Compensation

Worldwide

Eligibility

Expert

Experience

Jun 30, 2026

Posted

Open worldwide

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About OpenTrain

OpenTrain is the #1 platform for people building careers in AI training and data labeling. We connect experienced professionals with short-term and ongoing projects that teach AI systems how to reason, write, and operate in real workflows.

We help contributors consolidate opportunities, build a unified portfolio of AI training work, and grow a durable freelance career from anywhere in the world.

AI training work for legal teams — why it matters

AI models learn from examples and expert feedback. In legal applications, experienced attorneys teach systems to read contracts, spot risk, and draft or redline language with the judgement real transactions require.

This is an opportunity to apply your M&A experience directly to how future legal tools are designed and evaluated—work that is remote, flexible, and influential to product research and development.

The role

We are recruiting a part-time M&A Contract Review Attorney to evaluate and improve how AI models understand contract language and perform redlining, negotiation simulations, and risk assessment tasks.

You will translate your day-to-day M&A practice into objective evaluation criteria and concrete feedback that helps research and product teams improve model outputs and annotation guidelines.

What you'll do

Perform structured reviews of AI-generated contract analyses, redlines, and negotiation responses, then provide detailed, actionable feedback to improve accuracy and legal reasoning.

Design and apply grading rubrics and evaluation frameworks so model performance is measured consistently and defensibly across scenarios.

  • Assess AI responses to simulated M&A contract scenarios and redlining exercises.
  • Create objective scoring criteria and calibration examples for evaluation teams.
  • Perform negotiated redlines and explain preferred drafting or risk positions in written feedback.
  • Collaborate with cross-disciplinary teams to refine prompts, guidelines, and labeled data.

Requirements

You must meet the mandatory qualifications below. We will not consider applicants who do not meet these baseline requirements.

  • J.D. from an ABA-accredited law school.
  • Active bar admission in at least one U.S. jurisdiction.
  • Minimum of 2 years working in the M&A department of a corporate law firm.
  • Deep familiarity with standard M&A agreements, including APAs and SPAs.
  • Exceptional written and verbal communication skills and meticulous attention to detail.
  • Ability to translate legal expertise into clear, actionable feedback for AI systems.
  • Experience working with cross-disciplinary teams and a demonstrated interest in legal technology or AI is strongly preferred.

Work arrangement, time commitment, and pay

This is a remote, part-time contractor role with flexible scheduling. You may work from any location.

Time commitment is 20+ hours per week.

  • Employment type: Contractor, Part-time.
  • Pay: USD $80–$105 per hour (rate data provided by the project).
  • Language: English required.
  • Data type: Text labeling, including evaluation rating and text-generation tasks;

Who should apply and how it works

Apply if you are an experienced M&A lawyer who wants to shape legal AI tools and can commit to the requirements above. Prior exposure to AI, legal tech, or private equity experience is a plus but not mandatory.

Onboarding typically includes a brief qualification task, review of annotation guidelines, and calibration sessions with project leads. You'll provide written reviews and scores for model outputs and may perform simulated negotiation/redlining exercises as part of training datasets.

  • You will receive clear guidelines and examples for each task and collaborate with researchers to refine standards.
  • Feedback you provide will directly influence model behavior and downstream legal product features.