In-House Counsel

RemoteJobsOne; Dubai; Remote; AED 455,000 - 658,000; Part time

Job summary

RemoteJobsOne in United Arab Emirates is seeking a seasoned In-House Counsel for a part-time contractor role focused on technology transactions and AI-assisted contract review.

You will draft, review and redline MSAs, NDAs and ISDA derivatives, provide feedback to AI models, and collaborate with product and research teams to ensure rigorous evaluation frameworks.

Qualifications

• Minimum of 3 years of In-House experience focused on technology transactions.
• Experience negotiating MSAs, NDAs, ISDA's and Derivatives.
• Exceptional written and verbal communication with attention to detail.
• Strong analytical skills and ability to translate legal expertise into AI system feedback.
• Demonstrated ability to work with cross-disciplinary teams in fast-paced environments.
• Preferably exposure to AI, legal tech, or training initiatives.

Responsibilities

• Perform simulated contract negotiations and redlining exercises.
• Review AI responses to contract scenarios and provide expert feedback.
• Create evaluation frameworks and grading criteria for AI contract tasks.
• Collaborate with product and research teams to refine data, guidelines, and best practices.

Skills

In-House experience

Technology transactions

MSA negotiation

NDAs negotiation

ISDA & Derivatives

Analytical thinking

Excellent communication

Job description

IMPORTANT: after you apply, please check your email. We send you a link to complete your application it is not considered until that last step is done. If you do not see it, check your spam folder.

This is a fully remote position, open to candidates based in United Arab Emirates.

Pay: $90-130/hr

Role Title: In-House Counsel

Job Type: Contractor

Location: Remote

Job Summary: We are seeking seasoned In-House Counsels for a part-time role at the forefront of legal AI. This opportunity is for elite lawyers who want to help shape how advanced AI is trained, evaluated, and applied in real-world legal work, especially those who have deep experience with drafting, reviewing, negotiating, and redlining within the tech field.

In this role, you will review, assess, and contribute to contract redlining workflows used to train and evaluate state-of-the-art AI models. Your work will directly improve how these systems identify risk and interpret contract language to create tools with improved precision and legal judgment.

Key Responsibilities

1. Perform simulated contract negotiations and redlining exercises.
2. Review and assess AI responses to contract scenarios, providing expert feedback to improve model performance and output precision.
3. Create objective evaluation frameworks and grading criteria to assess AI performance on contract tasks with rigor and consistency.
4. Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions.

Required Skills and Qualifications

1. Minimum of 3 years of In-House experience focused on technology transactions, particularly negotiating MSAs, NDAs, ISDA's and Derivatives.
2. Exceptional written and verbal communication skills with meticulous attention to detail.
3. Strong analytical capabilities and ability to translate legal expertise into actionable feedback for AI systems.
4. Demonstrated commitment to innovation at the intersection of law and technology.
5. Experience working with cross-disciplinary teams in fast-paced environments.

Preferred Qualifications

1. Prior exposure to AI, legal tech, or training initiatives.

Why Join

1. This is an opportunity to work at the intersection of law and technology.
2. You will help define how AI is developed for a new generation of legal practitioners.
3. You will apply your experience in a high-impact research environment.

Compensation Structure

Compensation is task-based, meaning experts are paid per completed task that meets the project specifications. The average handling time (AHT) is approximately 3.5 hours per task, and the effective hourly rate will depend on the number of tasks completed and successfully signed off.