Founding ML Engineer – Engineering Assessment AI

You've built RL environments or graders, and hardware engineering doesn't scare you. Be the first ML hire on data nobody else has.

A pre-seed AI startup in San Francisco tests engineers on real engineering tasks for companies in space, defense, advanced manufacturing and robotics, so they can pick the right engineer for every project. It's already live with large deep-tech customers. As the founding ML engineer, you convert rubrics set by experts and recordings of real CAD and ECAD sessions into training environments, scoring models and reward signals, and package them as post-training data for frontier labs.

What you'll own

• RL environments, graders and reward signals built from real engineering work
• Datasets shaped for post-training at frontier labs
• Evals, LLM-as-a-judge setups and the data pipelines behind them
• A real say in the research and product roadmap, in a role that grows with the company

Must-haves

• Real experience with RL environments: building, selling or using them for post-training (RAG or prompt-engineering work alone won't fit)
• A grounding in hard tech: ML at a hardware company, close work with electrical or mechanical engineers, or an EE/ME degree
• Knowledge of ML around CAD, ECAD, 3D or other hardware data
• 3+ years of experience
• Working from the San Francisco office, 9-9-6 (moving to SF is expected if you live elsewhere)
• US citizen, green card holder, refugee or asylee, or a straightforward TN case

Bonus points

• Post-training experience at a scaling AI company or a frontier model team
• An EE or ME background that moved into ML
• A time you looked past your brief and simply did what the project needed

What's in it for you

• $150k–$250k base + 0.2–0.7% equity
• Founding ML seat in a new field, on proprietary data
• Real ML research, not an applications job

Good to know

• Full-time, on-site in San Francisco, 9-9-6
• No visa sponsorship beyond the options listed above
• Process: profile check → first call with the hiring lead → home assignment → final interview → offer

When you apply, please include

• Your CV (PDF), your LinkedIn URL, and GitHub or papers if you have them
• An RL environment, grader or reward signal you worked on: what it measured and how dependable its scores were
• A few lines on why this work interests you
• Your current location, and confirmation that on-site San Francisco (or relocating) works for you
• Your visa or citizenship status
• Your base salary expectation, your notice period, and any other interview processes you're in

Does this sound like you? Apply now. If you're a match, we'll be in touch within 3 working days.

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