Transactions Associate

What you would do

• Execute simulated contract redlining and negotiation scenarios to train and evaluate AI systems
• Review AI responses to technology contract questions and identify errors in legal interpretation
• Create objective evaluation criteria and grading rubrics for assessing AI performance on contract tasks
• Develop and refine playbooks and guidelines that teach AI to apply sound legal judgment to MSAs, NDAs, and SaaS agreements

Who they want

• Law degree from ABA-accredited law school with active licensing in at least one U.S. state
• Minimum 3 years focused on technology transactions, including negotiating and drafting MSAs, NDAs, data protection agreements
• Exceptional written and verbal communication with meticulous attention to detail and strong analytical skills
• Demonstrated ability to work with cross-functional teams in fast-paced environments; prior legal tech or AI exposure is preferred

Main skills

What the interview asks about

1. 1.Identifying AI errors in contract interpretation

   You must catch flawed reasoning, not just wrong answers; recognizing when an AI applies a rule correctly but in the wrong context teaches the system more effective legal judgment.

   For example: “An AI redlines a cap to exclude confidential breaches, reasoning they're more damaging than IP. Your policy excludes IP but caps confidentiality normally. How would you grade this and what feedback helps it distinguish principle from strategy?”

2. 2.Designing evaluation frameworks and scoring

   Playbooks must be repeatable and unambiguous; a vague rubric produces inconsistent training data, while precise criteria teach the AI to reason like a skilled negotiator.

   For example: “You're evaluating an AI on Data Processing Agreement vendor edits. It might define 'personal data' correctly but miss when audit scope is too broad. Create a 3-5 level scoring scale for one DPA clause separating correctness from judgment.”

3. 3.Simulated negotiation and fallback strategy

   Teaching AI to negotiate requires showing when to hold firm versus when to concede; your ability to explain the trade-off reveals whether you understand true deal-making versus rigid rules.

   For example: “In a SaaS negotiation, you're vendor with non-negotiable 2-year lock-in. The AI counters with 1-year plus auto-renewal. How would you respond while preserving relationship, and what would you teach the AI about negotiation?”

4. 4.Technology-specific risk assessment

   Tech contracts involve specialized risks (data liability exposure, IP indemnity scope, SaaS service levels) that require domain knowledge to evaluate properly; superficial rule application leads to poor AI judgment.

   For example: “An AI reviewing a cloud agreement misses that the data residency clause conflicts with required audit rights. It flagged it as 'standard' but missed the downstream problem. Explain how you'd identify this gap and what training would help?”

5. 5.Translating legal judgment for AI teams

   ML engineers don't speak law; you must convert intuitive legal expertise into explicit, measurable criteria that a non-lawyer can implement in the model.

   For example: “Your team asks how the AI should weight indemnification versus liability clauses. Explain to an engineer why neither is inherently 'more important' and what factors should drive AI prioritization.”

A task you may get

Create a rubric for assessing an AI on a single tech contract task (e.g., marking up MSA liability clauses). Include scoring criteria, common errors, and how you'd prevent bad judgment.

How to prepare

• Review a recent tech deal and document three decisions: where you held firm, conceded, and why. Explain principle versus pragmatism.
• Prepare an example of AI making a technically correct legal interpretation but missing real-world context or business judgment.
• Prepare a complex tech clause (liability caps plus indemnification carve-outs) and explain how you'd teach an AI to recognize clause interdependencies.

The facts

Pay

$80–130/hr

Open to

Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia

Field

Law

Role type

Specialist

Posted

8/12/2026

Places left

100

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