Engineering Expert

Weekday (YC W21)

. Design, develop, and validate complex engineering simulation problems for training and evaluating advanced AI models and coding agents .

Posted 10/20/2026contractRemote • IndiaSeniorLead💰 $500 per weekWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and validating engineering simulations for AI model training, with strong proficiency in Python programming and open-source engineering tools. Capable of analyzing complex engineering problems while ensuring adherence to physical constraints and validation standards.

Highest-signal resume keywords

Python ProgrammingEngineering Simulation DevelopmentOpen-Source Engineering ToolsAI Evaluation MethodologiesAnalytical Problem-Solving

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Engineering Design ExperienceSimulation-Based WorkflowsNumerical ConvergenceBoundary ConditionsPhysical ConstraintsDebugging SkillsAutomated Grading MechanismsModel Execution AnalysisCustom Test BenchesEmpirical Model Performance

Soft Skills

Analytical SkillsProblem-Solving SkillsTechnical Documentation SkillsIndependent Work AbilityCollaboration Skills

Tools & Technologies

NgspicePySpiceOpenFOAMFEniCSxCalculiXPython-ControlOpenModelicaCadQueryBuild123dCantera

Certifications & Qualifications

Master's DegreePhD

Industry Keywords

Electrical EngineeringMechanical EngineeringAerospace EngineeringAI Coding AgentsSimulation Problems

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design, develop, and validate complex engineering simulation problems for training and evaluating advanced AI models and coding agents
Develop realistic, multi-constraint problems across Electrical, Mechanical, and Aerospace Engineering
Create measurable objectives, validated reference solutions, and objectively verifiable automated grading mechanisms
Build, configure, execute, and validate simulation-based problem environments using open-source engineering tools
Develop Python scripts, custom test benches, and utilities to automate simulations and verify results
Ensure simulation outputs satisfy physical plausibility, dimensional consistency, boundary conditions, convergence requirements, and domain-specific constraints
Troubleshoot simulation failures, numerical instability, dependency issues, and computational errors
Evaluate LLMs and coding agents across repeated engineering problem-solving trials
Analyze execution trajectories, tool interactions, simulator feedback, and intermediate outputs to identify reasoning and tool-use failures
Calibrate task difficulty and refine benchmarks using empirical model performance and evaluation results
Collaborate with AI researchers, technical leads, and subject-matter experts to integrate validated benchmarks into AI evaluation workflows

Requirements

What you’ll need

Master's degree or PhD in Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or a closely related technical discipline
At least 10 years of practical engineering design experience
Strong Python programming and scripting skills
Hands-on proficiency with at least one relevant open-source engineering simulation or design tool, such as ngspice, PySpice, OpenFOAM, FEniCSx, CalculiX, python-control, OpenModelica, CadQuery, build123d, Cantera, or Gmsh
Experience developing, executing, debugging, and validating simulation-based engineering workflows
Familiarity with modern LLMs, AI coding agents, and AI evaluation methodologies
Ability to analyze model execution logs and identify reasoning, planning, and tool-use failures
Strong understanding of physical constraints, units, boundary conditions, numerical convergence, and engineering validation
Excellent analytical, problem-solving, and technical documentation skills
Ability to work independently and deliver technically accurate results within defined timelines
Approximately 40 hours per week availability
At least 4 hours of daily overlap with US Pacific Time
Weekend on-call availability
Personal desktop or laptop
Stable, high-speed internet connection

Benefits

Comp & perks

Remote work arrangement
Short-term contractor engagement of up to 24 weeks
Part-time engagement may be acceptable where agreed
Weekend on-call availability
Flexible work location among approved eligible locations
Opportunity to work on AI model training and evaluation benchmarks