Founding AI Engineer

United States·San Franciscofull-timemid

Machine Learning EngineerData

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Quick Summary

Overview

Mason AI is on the frontier of agentic AI for the built world: we're the technical leaders at building AI that checks blueprints are buildable and safe.

Technical Tools

Machine Learning EngineerData

Mason AI is on the frontier of agentic AI for the built world: we're the technical leaders at building AI that checks blueprints are buildable and safe.

We’re looking for a Founding AI/ML Engineer to help us scale what works for residential projects in California to all construction across the country.

You will go deep on the cutting edge of optimizing multimodal AI agents; you will hillclimb on computer vision evals; and you will run time-boxed experiments that are high-risk, high-reward. You will become a domain expert on the ADA, Wildland-Urban Interfaces, and other wonky parts of the building code. Your work will directly affect the built environment of California within the first month of joining.

You will have a massive impact on the product, the company's future, and the country's, as we accelerate American construction. Join us.

Either a SWE passionate about growing as an ML/AI Engineer, or a ML Engineer who's fast at prototyping.
You can run experiments quickly and develop research taste.
You're mission-oriented and looking for your life's work. You care about America's ability to build.
You love ownership and don't mind ambiguity or unblocking yourself.
You have the curiosity of a policy wonk to digest building codes and architectural diagrams.

Responsibilities

~1 min read

→60+% of your time will be spent improving the accuracy of our multimodal agents that check blueprints for building code compliance. This can range from building autoresearch to improving our evals to running sweeps on new experimental features you build. Your research taste will direct this work.
→Talk to users and dogfood the product weekly
→Wear hats as needed on topics ranging from running computer vision experiments, lightweight data engineering, building internal tooling, improving the webapp, and more.

Fail fast and cheap. Slugging percentage matters more than batting average.
We can make a lot of mistakes as long as we get a few things right.
A written culture - we document our thinking on the few things that matter most.
Internal tooling is a first-class use of time.
In-person work is a must.

What We Offer

~1 min read

✓Competitive salary ($160k-$210k).

✓Generous early-stage equity (0.75%-2%).

✓Free lunch and dinner (ordered at 6 pm) at the office.

✓Fully paid health insurance with over $1000/year in employer contributions to an HSA.

✓A 401(k) that accepts pre-tax and Roth contributions.

✓A commuter benefits account and a monthly wellness benefit.

✓Opportunity to shape the future of our country.

Our interview process aims to be lightweight and respect candidates' time. We expect it to take 2 weeks or less.

Intro call (30 min) - Informal chat on your interests and experience
AI Coding & Culture interviews (90 min)
- Designed to mirror real work you'd do at Mason, you'll build a prototype for a slice of our product. AI coding tools (e.g. claude code, conductor, wisprflow) are strongly encouraged.
- Learn more about how you've navigated challenges in (or outside) of work.
Paid work trial (3 days) - We'll set you up to run evals for our multimodal agent on our repo, and we'll ask you to generate hypotheses for experiments to run, run those experiments, and then analyze them. You'll be evaluated on your speed at running thoughtful experiments. We're flexible on scheduling, and usually run these over the weekend to minimize impact on your weekday schedule.
Full-time offer 🎉

Location & Eligibility

Where is the job

San Francisco, United States

On-site at the office

Who can apply

About this job

Pay per year: $160,000–$210,000 a year; 12% below the $210,000 median for Machine Learning Engineer jobs on Jobera (862 salaries).
Who can apply: United States
Posted: 211 days ago
Employer: has 1 open job on Jobera.