AI Trainer: Electrical Engineering

Brunel•New York, NY

2d•$40 - $125•Remote

About The Position

Brunel is partnering with DataAnnotation to connect experienced Electrical and Electronics Engineers with an innovative AI training program. This opportunity brings together hands-on engineering expertise and artificial intelligence to help evaluate and improve how AI models handle real circuit and electronics work. Selected experts will contribute their knowledge to the development of high-quality, technically accurate AI systems. This opportunity is designed for practicing engineers who currently design, simulate, and debug real hardware, across analog, RF and mixed-signal, power electronics, embedded and controls, signal processing, photonics, and board-level design.

Requirements

2+ years of industry experience in circuit or electronics engineering, currently or recently doing the technical work as an individual contributor.
Background in electrical engineering, electronics engineering, or a closely related discipline; Bachelor’s degree or higher (in progress accepted).
Hands-on experience matters more than degrees or licenses.
Strong command of circuit analysis and design tradeoffs, and the ability to defend component-level and system-level decisions.
Fluency in the tools of the field: circuit simulation (SPICE or similar), schematic capture and PCB tools, lab instruments, and MATLAB or Python for engineering analysis.
Full professional/native-level written English, with the ability to write a precise, unambiguous technical problem and explain why an answer is wrong, not only that it is.
Comfort working independently in a fully remote environment on long-form, self-directed tasks.
Strong attention to detail and the judgment to distinguish a well-reasoned engineering answer from a plausible-sounding but incorrect one.
General familiarity with AI/LLM tools; user-level familiarity is sufficient.

Responsibilities

Design open-ended tasks drawn from their own engineering practice, such as specifying a circuit, stabilizing a control loop, closing a link budget, sizing a power stage, or debugging a signal chain.
Build tasks from files a practitioner actually handles (schematics, simulation setups, datasheets, board files, measurement data).
Run tasks through frontier AI models and grade them against a professional standard.
Compare model outputs.
Write grading rubrics.
Flag concrete failures such as wrong assumptions, sign and unit errors, unphysical results, invalid component or topology choices, fabricated datasheet values, and misread schematics or plots.