Your domain expertise will directly shape how AI understands and reasons through complex engineering problems — from circuit analysis to power systems and beyond.
This is a fully remote, flexible contract role where your technical knowledge makes a real impact on the future of AI.
Why Join Us
Work on some of the most advanced AI projects in the world alongside top research labs.
Fully remote and flexible — set your own hours and workload.
Freelance perks: autonomy, variety, and global collaboration.
Direct, meaningful impact on how AI understands engineering.
Potential for ongoing work and contract extension.
Responsibilities
Design Complex Engineering Problems — Create advanced, domain-specific problems spanning circuit analysis, power systems, signal processing, electromagnetics, and control systems to rigorously test AI performance.
Author Ground-Truth Solutions — Develop precise, step-by-step technical solutions and benchmark responses that guide AI learning.
Audit Technical Accuracy — Evaluate AI-generated code, circuit diagrams, and mathematical proofs for correctness, safety, and adherence to engineering standards such as IEEE and NEC.
Refine AI Reasoning — Identify logical flaws in AI-generated engineering reasoning and provide structured, actionable feedback to improve model performance.
Stress-Test AI Knowledge — Challenge advanced language models across embedded systems, signal processing, robotics, and engineering simulations, documenting failure modes to harden model reasoning.
Required skills
Strong foundational expertise in one or more core areas: VLSI, control systems, telecommunications, power electronics, or signal processing.
Able to communicate highly technical concepts clearly and concisely in written form.
Detail-oriented — precise and methodical when reviewing mathematical equations, circuit logic, and technical documentation.
Self-motivated and comfortable working independently on an asynchronous, flexible schedule.
Preferred skills
Experience with data annotation, data quality assessment, or evaluation systems.
Familiarity with AI or machine learning workflows.
Background in academic or applied engineering research.
Experience and education
Pursuing or holding a Master's or PhD in Electrical Engineering, Electronic Engineering, or a closely related field.
Thriveth makes AI data-training work easier to find, understand, and navigate. We replace uncertainty with clear opportunities, realistic expectations, and insights from real application journeys.