AI Trainer – Electrical Engineering

Planet Pharma; San Francisco (CA); Remote; USD 120,000 - 180,000; Full time

Job summary

Planet Pharma is seeking experienced electrical and electronics engineers to evaluate how frontier AI models handle real engineering work, such as specifying a circuit, stabilizing a control loop, closing a link budget, sizing a power stage, and debugging a signal chain.

You will work with realistic professional files, assemble tasks, run them through frontier AI models, and evaluate results against professional standards. The role is remote, long-form, and self-directed.

Qualifications

• 2+ years of industry experience in circuit or electronics engineering.
• Bachelor’s degree or higher preferred; hands-on experience outweighs degrees.
• Strong command of circuit analysis and design tradeoffs.
• Fluency with circuit simulation (SPICE and similar) and schematic/PCB tools.
• Proficient in MATLAB or Python for engineering analysis.

Responsibilities

• Design challenging, realistic engineering problems from your expertise with supporting files (schematics, data, boards).
• Run problems through frontier AI models and evaluate results against professional standards.
• Compare two model versions on identical prompts and document performance differences.
• Critique model-generated derivations, schematics, component choices, simulations, and calculations; write grading rubrics.
• Flag concrete failures with evidence: wrong assumptions, sign errors, unphysical results, misread data.
• Contribute across analog, RF and mixed-signal domains and review tasks built by others.

Skills

Circuit analysis

Engineering judgment

English communication

Education

Bachelor in electrical/electronics engineering

Tools

SPICE

Schematic capture

PCB tools

MATLAB

Python

Job description

About the role

is looking for experienced electrical and electronics engineers to evaluate how frontier AI models handle real engineering work: specifying a circuit, stabilizing a control loop, closing a link budget, sizing a power stage, debugging a signal chain. The models can already talk fluently about electronics; what they cannot yet do reliably is the actual work. You bring the judgment you have built over years of designing, debugging, and shipping hardware. We bring the engineering problems the models cannot yet solve on their own.

This is a new and growing area for our company, focused on teaching AI to reason correctly about circuits, systems, and physical constraints. In this role, you will design challenging, realistic tasks drawn from your own practice, run them through frontier AI models, and evaluate what comes back against a professional standard. Your engineering judgment, your standard for what counts as a correct answer, and the written rationale behind them become the signal that shapes the next generation of engineering tools.

You will work with realistic professional files, the kind a practitioner in your field actually handles, which you assemble yourself. Some tasks are compact, built around a handful of files; others are larger scenarios that take several days to build. In every case the goal is the same: a task a competent professional in your field would complete correctly and a frontier model currently gets wrong.

This is not a traditional electrical engineering role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long-form and self-directed, and clear written reasoning matters as much as technical depth.

Responsibilities

Design challenging, realistic engineering problems drawn from your own professional expertise: a circuit to specify, a control loop to stabilize, a link budget to close, a power stage to size, a signal chain to debug, along with the supporting files an engineer would need (schematics, simulation setups, datasheets, board files, measurement data), which you author yourself.

Run those problems through frontier AI models and evaluate the deliverable they produce against the standard you would hold a colleague to.

Compare two model versions on identical prompts and supporting material, decide which performed better, and document where each fell short.

Critique model-generated derivations, schematics, component selections, simulation setups, and design calculations, and write detailed grading rubrics plus the reasoning behind your judgment.

Flag concrete failures with evidence: unstated or wrong assumptions, sign and unit errors, unphysical results, invalid component or topology choices, misapplied approximations, fabricated datasheet values, misread schematics and plots, and off-brief interpretation of the ask.

Contribute across analog, RF and mixed-signal circuits, electromagnetics, microwaves and antennas, control and systems, electronic devices and materials, signal processing, photonics and optoelectronics, and electric power and energy systems, and review and refine tasks built by other engineers.

Domain qualifications

2+ years of industry experience in circuit or electronics engineering; 5 or more years is a plus. Any specialty: analog, RF and mixed-signal, power electronics, embedded and controls, signal processing, electromagnetics and antennas, photonics and optoelectronics, or electric power and energy systems.

Background in electrical engineering, electronics engineering, or a closely related discipline. In progress Bachelor’s degree or higher; hands‑on experience outweighs degrees and certifications.

Strong command of circuit analysis and design tradeoffs, and the ability to defend component‑level and system‑level decisions.

Fluency in the tools of your specialty: circuit simulation (SPICE and similar), schematic capture and PCB tools, lab instruments, and MATLAB or Python for engineering analysis.

Meticulous: you check your own work and enjoy finding the edge case that breaks an assumption.

A current, in-progress, or completed Bachelor’s in electrical/electronics engineering or a related field is preferred but not required.

General requirements

Hands‑on practitioner: you currently do (or recently did) the work yourself at an individual‑contributor level, not solely in a managerial capacity.

Full professional or native‑level written and spoken English; you can explain complex professional reasoning clearly and articulate why a result is wrong, not only that it is.

Comfort with ambiguity and attention to detail; you verify what a document claims against the underlying numbers, sources, or facts.

Capable of interpreting feedback, judging which parts are correct, and applying it without hand‑holding.

General familiarity with AI and LLM tools: you can tell a well‑reasoned answer from a plausible‑sounding but incorrect one, or can pick these tools up quickly.

Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing).

Available at least 10 hours per week, with no weekly maximum; this project prioritizes 30‑40 hrs/week and full‑time hours are available.

Based in the United States, Canada, the UK, Australia, Ireland, or New Zealand.