Remote Engineering Expert; PhD, Master's, or Olympiad Participants - AI Trainer

Overview

Mercor is collaborating with a leading AI research lab on a project to advance engineering problem-solving at the frontier of artificial intelligence. We are seeking experienced engineers with strong backgrounds in analytical problem design, real-world systems modeling, or competitive engineering problem-solving (university competitions, design challenges, or advanced coursework). The goal of this project is to create novel, rigorous, and multi-step engineering problems that challenge cutting-edge AI models—specifically those that cause the AI to fail or demonstrate reasoning limits—while supporting the improvement and training of advanced AI systems.

This is a short-term, high-impact opportunity for engineering professionals eager to apply their deep technical expertise to frontier AI research, with the possibility of project continuation based on performance.

• Design original, advanced engineering problems that require creative reasoning, multi-domain integration, and precise analytical thinking.
• Evaluate AI-generated engineering solutions for technical accuracy, completeness, and adherence to physical and practical constraints.
• Identify logical gaps, incorrect modeling assumptions, or computational inaccuracies.
• Provide complete, clear, and well-documented solutions and derivations using LaTeX formatting for equations and schematics.
• Maintain high standards of technical precision, rigor, and conceptual depth.

Note:

Applicants must be proficient in creating and formatting technical documents using LaTeX. You are a strong fit for this project if you have one or more of the following:

• Advanced degree (or equivalent experience) in mechanical, electrical, civil, aerospace, or chemical engineering, or a related field.
• Experience designing or grading complex analytical or design-focused problems (university level or professional exams).
• Participation in or mentorship for engineering design or modeling competitions is preferred (e.g., Formula SAE, Shell Eco-marathon, ASME / IEEE / AIChE contests, robotics challenges, or design sprints).
• Research or teaching experience involving applied problem formulation and quantitative modeling.
• Deep understanding of core engineering principles and real-world systems.
• Ability to communicate step-by-step reasoning, assumptions, and derivations clearly.
• Strong intuition for problem difficulty, structure, and conceptual progression.
• Detail-oriented verification of results and internal consistency.

• Time commitment:
  Minimum 20 hours / week; up to 40 hours / week available
• Duration:
  Approximately 2 months, with strong potential for extension
• Rolling onboarding; start typically within 1–2 days after approval