Electronics Engineer & AI Trainer: Judge AI on Circuits

DataAnnotation; Cedar Rapids (IA); Remote; USD 45,000 - 65,000; Full time

Benefits offered by this job

Flexible work

Remote work

Project-based work

Job summary

DataAnnotation is seeking an experienced Electrical Engineer to help teach frontier AI models to reason about circuits, systems, and physical constraints. You bring years of hardware design, debugging, and shipping experience, while we provide the AI challenges that push the tools forward.

You will design realistic problems, run them through AI models, compare versions, critique model outputs with evidence, and flag failures with solid reasoning.

Qualifications

• 2+ years of industry experience in circuit or electronics engineering.
• Background in electrical/electronics engineering or related discipline.
• Ability to defend component- and system-level decisions.
• The ability to articulate why an answer is wrong, not only that it is.

Responsibilities

• Design challenging, realistic engineering problems for circuits, controls, and power stages.
• Run those problems through frontier AI models and judge the output to a professional standard.
• Compare two model versions on identical prompts and call which performed better.
• Critique model-generated derivations, schematics, component selections, and calculations, with justification.
• Flag concrete failures with evidence: wrong assumptions, sign and unit errors, unphysical results.

Skills

Circuit analysis

RF & mixed-signal circuits

Control systems

Power electronics

AI tools familiarity

Reasoning & justification

Education

Bachelor’s degree in Electrical Engineering

Tools

AI tools

Job description

DataAnnotation is seeking an experienced Electrical Engineer to help teach frontier AI models to reason about circuits, systems, and physical constraints. You bring years of hardware design, debugging, and shipping experience, while we provide the AI challenges that push the tools forward.

You will design realistic problems, run them through AI models, compare versions, critique model outputs with evidence, and flag failures with solid reasoning.