Electronics Engineer & AI Trainer: Shape AI Reasoning

DataAnnotation; Indiana (PA); Remote; USD 55,000 - 172,000; Full time

Benefits offered by this job

Flexible schedule

Remote work

Project opportunities

Job summary

DataAnnotation is seeking an experienced Electrical Engineer to evaluate how frontier AI models handle real electrical and electronics engineering work. You bring your engineering judgment and written rationale to shape the next generation of engineering tools.

This remote role focuses on teaching AI to reason about circuits, systems, and physical constraints, with compensation varying by task difficulty and specialization.

Qualifications

• Fluency in English (native or bilingual).
• 2+ years of industry experience in circuit or electronics engineering.
• Bachelor’s degree in electrical engineering or related discipline (preferred).

Responsibilities

• Design challenging, realistic engineering problems from your practice: a circuit to specify, a control loop to stabilize, a power stage to size.
• Run those problems through frontier AI models and judge the output against your professional standard.
• Compare two model versions on identical prompts and call which performed better, and where each fell short.
• Critique model-generated derivations, schematics, component selections, and calculations, and write the reasoning behind your call.
• Flag concrete failures with evidence: wrong assumptions, sign and unit errors, unphysical results, invalid topologies, fabricated datasheet values, or off-brief interpretation.

Skills

English fluency

Circuit analysis

Education

Bachelor’s degree in Electrical Engineering

Job description

DataAnnotation is seeking an experienced Electrical Engineer to evaluate how frontier AI models handle real electrical and electronics engineering work. You bring your engineering judgment and written rationale to shape the next generation of engineering tools.

This remote role focuses on teaching AI to reason about circuits, systems, and physical constraints, with compensation varying by task difficulty and specialization.