LLM Engineering Expert- Simulation & Design

Dover; United States; Remote; USD 1,200 - 2,500; Part time

Job summary

Careerflow Human Data Labs is hiring experienced AI Evaluation Engineers to author and validate simulation-based engineering design problems that train and evaluate state‑of‑the‑art AI agents. You will operate across Electrical, Mechanical, Aerospace, and related domains, creating multi‑constraint tasks and automated graders to push frontier models.

The role requires weekend on‑call availability as a contractor, with 30‑40 hours per week and overlap with PST.

Qualifications

• 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 with strong Python scripting.
• AI Evaluation & Failure Diagnostics: Hands‑on experience with modern LLMs/coding agents and evaluation concepts, ability to audit trajectory logs.
• Domain Rigor & Precision: Attention to physical plausibility, unit consistency, boundary conditions, convergence criteria, and documentation.
• Availability & Commitment: Weekend on‑call availability (part‑time engagement acceptable).
• Technical Infrastructure: Remote setup with a stable internet connection.

Responsibilities

• Model‑Breaking Problem Design: Author self‑contained engineering design tasks with constraints, targets, and autograders.
• Environment & Simulation Integration: Build, run, and validate environments using open‑source tools and Python test benches.
• Trajectory Analysis & Failure Mode Taxonomy: Evaluate agent outputs and logs to identify failure modes.
• Difficulty Calibration & Benchmark Refinement: Refine problem difficulty based on performance data.
• Cross‑Functional Collaboration: Work with AI researchers and domain experts to integrate benchmarks into evaluation pipelines.

Skills

Engineering design

LLM evaluation concepts

Python scripting

Simulation interpretation

Education

Master’s degree or PhD in Electrical, Mechanical, Aerospace

Tools

ngspice

PySpice

OpenFOAM

FEniCSx

CalculiX

python-control

CadQuery

build123d

OpenModelica

Cantera

Gmsh

Job description

Careerflow Human Data Labs partners with AI companies to bring real-world professional expertise into their products.

Role Overview We are looking for 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.

Job Requirements:

• 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, CadQuery, build123d, OpenModelica, 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.

Job Responsibilities:

• 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.

• Location: Bangladesh, India, Indonesia, Egypt, Ghana, Kenya, Nigeria, Turkey, Brazil, Colombia.

Domains

• Electrical Engineering
• Mechanical Engineering
• Aerospace Engineering

Education & Experience

• Bachelor's degree or equivalent practical experience in any field.
• Experience in AI evaluation, data annotation, content review, quality assurance, or a related analytical role is preferred but not required.

Offer Details

• Commitments Required: 30-40 hours per week with 4 hours of overlap with PST.
• Engagement type: Contractor
• Engagement Length: upto 24 weeks

Evaluation Process

• Shortlisted candidates will be sent a Job Interest Form.
• Finalized talents will go through delivery review & proceed further accordingly.

Engagement Length

• Up to 24 weeks, Full‑time (8 hours/day) 40 hours per week. Overlap 4 hours with PST (Weekend on‑call availability required) Rate Range: $500 per task (considering 30-40 hours of AHT per task)

This is a Pay-Per-Task model which means you'll get paid per task for an estimated hourly rate.

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