Engineering Expert

Role Overview

We are seeking experienced AI Evaluation Engineers (Engineering Simulation & Design) to author and validate "model-breaking," simulation-based engineering design problems to train and evaluate state-of-the-art AI agents. Operating across major engineering disciplines including Electrical, Mechanical, Control Systems, Aerospace, Systems, and Robotics—you will create complex, multi-constraint tasks where AI agents must interpret requirements, navigate trade-offs, configure open-source simulation tools, diagnose failures, and iterate toward valid solutions.

You will analyze agent execution logs, expose systemic reasoning gaps, and build automated, objective graders to elevate frontier model performance.

• Education & Expertise: Master’s degree or PhD in Electrical, Mechanical, Aerospace, with 10+ years of hands-on engineering design experience.
• Simulation Tooling: Proficiency with at least one domain-relevant open-source simulation package (e.g., ngspice, PySpice, OpenFOAM, FEniCSx, CalculiX, python-control, Cad Query, build
  123d, Open Modelica, Cantera, Gmsh) combined with strong Python scripting skills.
• AI Evaluation & Failure Diagnostics: Hands-on experience with modern LLMs/coding agents and evaluation concepts (pass@k, failure-mode analysis, nondeterministic behavior), with the ability to audit trajectory logs and isolate core reasoning/tool-use failures.
• Domain Rigor & Precision: Uncompromising attention to physical plausibility, unit consistency, boundary conditions, convergence criteria, and technical documentation.
• Availability & Commitment: Talent must have weekend on-call availability (part-time engagement is acceptable).
• Technical Infrastructure: Personal desktop/laptop equipped with a stable, high-speed internet connection in a remote setup.

• Model-Breaking Problem Design: Author original, self-contained engineering design tasks with competing constraints, explicit optimization targets, validated reference solutions, and objective autograders.
• Environment & Simulation Integration: Build, run, and validate problem environments using open-source simulation tools and custom Python test benches.
• Trajectory Analysis & Failure Mode Taxonomy: Evaluate coding agent outputs and execution logs across repeated trials to identify systemic failure modes (e.g., misinterpreting simulator feedback, premature design convergence, physically impossible geometries).
• Difficulty Calibration & Benchmark Refinement: Iteratively refine problem difficulty based on empirical model performance data without introducing ambiguity or missing information.
• Cross-Functional Collaboration: Partner with AI researchers, pod leads, and domain experts to integrate high-rigor benchmarks into the model evaluation pipeline.

• Electrical Engineering
• Mechanical Engineering
• Aerospace Engineering

• Master or PhD from relevant engineering domain is a must for this position.
• Experience in AI evaluation, data annotation, content review, quality assurance, or a related analytical role is required.

• Commitments
  
  Required:
  
  40 hours per week with 4 hours of overlap with PST.
• Engagement type:
  Contractor
• Engagement Length: upto 24 weeks