Mason AI — Founding AI Engineer

Type: Full-time | On-site | San Francisco, CA Compensation: $160,000–$200,000 + 0.5–2% equity Hiring count: 1 Visa sponsorship: None available. Relocation supported (per outreach template). Reports to: Not specified on role page — founding role; team of 3 moving to 2 as the CTO transitions out.

About Mason AI

Mason AI is building agentic AI for the built world — multimodal agents that review construction blueprints for buildability and code compliance, becoming domain experts on rules like the ADA and Wildland-Urban Interface. The company grew out of SFYIMBY, the pro-housing organization in San Francisco, and its mission is to solve the housing crisis: if Mason wins, the cost of building housing drops and rents get more affordable. It is the technical leader in its space with the most progress of any competitor.

Founded: 2025 | Team size: Seed 1–10 (currently 3, moving to 2) | Total funding: $4.3M Industry: Property Tech Website: withmason.ai Office: San Francisco, CA

Why Candidates Should Join

• Work directly on housing: One of the very few places in tech where an engineer works directly on the housing crisis — a mission shared even by its investors, and the company's single strongest draw.
• Founding ownership: A large chunk of equity (0.5–2%) and a founding-engineer mandate; the company is explicitly betting its future on this hire and the next.
• Technical leadership + strong demo: Furthest-along player in its space, with a product demo that reliably gets strong engineers excited.
• Comp + full benefits: $160–200K base plus free lunch and dinner at the office, fully paid health insurance with $1,000+/yr employer HSA contributions, commuter ($100/mo) and wellness ($50/mo) benefits.

Intake Call Summary

• Deeply self-directed founding engineering role at a tiny company: currently 3 people, moving to 2 as the CTO transitions out.
• Core of the job is running experiments end to end on Mason's eval framework — generating hypotheses, implementing them, and rigorously analyzing results to improve multimodal blueprint-compliance agent accuracy.
• Two profiles have worked here: (1) an exceptional SWE who wants to break into AI research, and (2) an AI/ML engineer already established in the space.
• Heads-down and technical: only ~2 hrs/week talking to users (occasionally on-site) — this is not a forward-deployed role.
• High intensity: ~55 hrs/week with weekend availability, in person in SF every day, in exchange for large ownership.
• The company is explicitly betting its future on this hire and the next.

The Role

A deeply self-directed founding AI engineering role owning the full experiment loop to improve the accuracy of Mason's multimodal blueprint-compliance agents.

What You'll Be Doing

• Spend the majority of your time improving the accuracy of Mason's multimodal blueprint-compliance agents — building autoresearch, improving evals, and running sweeps on experimental features you design.
• Own the full experiment loop: coming up with ideas, implementing them, and rigorously analyzing results on the eval framework.
• Become a domain expert on the parts of the building code that matter (ADA, Wildland-Urban Interfaces, structural and fire safety).
• Wear hats as needed: computer-vision experiments, lightweight data engineering, internal tooling, and webapp improvements.
• Weekly user contact and product dogfooding, roughly two hours a week.

Tech stack: Tech-stack agnostic; TypeScript front-end and back-end referenced. AI coding tools (e.g. Claude Code) used in the process.

Requirements

• Independent and self-unblocking, able to direct your own work with minimal oversight
• High velocity, fast at prototyping and shipping experiments
• Rigorous about evaluation, with genuine research taste to steer high-risk, high-reward work
• Either a strong SWE eager to grow into ML/AI, or an ML/AI engineer already fast at prototyping
• Tech-stack agnostic (comfortable across TypeScript front-end and back-end)
• Genuine mission alignment with solving the housing crisis
• Able to work in person in San Francisco, ~55 hours/week with weekend availability

Green Flags

• In-network candidates and warm referrals (historically the strongest source of hires)
• Deep, authentic mission alignment on housing, which has let Mason land candidates it would otherwise have no shot at
• Self-driving car company backgrounds (Waymo, Zoox): fast to pick up the frameworks and instinctively understand why evaluation rigor matters
• Highly regulated application-layer AI (medical, legal) and climate-tech backgrounds
• Teams where everyone has worked on successful AI products before
• Strong schooling
• The curiosity of a policy wonk to digest building codes and architectural diagrams

Red Flags

• Mercenaries: candidates whose motivation is simply breaking into the startup scene rather than the mission. Some have passed the coding challenge and still been turned down for this reason
• Big-tech backgrounds without genuine appetite for startup pace; the company has been repeatedly disappointed here
• Anyone who needs structure and direction rather than unblocking themselves
• Candidates who cannot or will not commit to in-person SF work at startup intensity

Role Details

• Salary — $160,000–$200,000
• Equity — 0.5–2%
• Experience — 2–8 years
• On-site policy — In person in SF every day, ~55 hrs/week with weekend availability
• Visa sponsorship — None available; relocation supported
• Employment type — Full-time
• Location — San Francisco, CA

Screening Questions

None provided on the Contrario role page.

Interview Process

Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — Intro Call (30 min) — Informal chat on interests and experience. Stage 3 — AI Coding & Culture Interviews (90 min total, back to back) — Build a prototype for a slice of Mason's product using AI coding tools (e.g. Claude Code), sharing your screen, plus a conversation on how you've navigated challenge. Stage 4 — Paid Work Trial (3 days) — Run evals for the multimodal agent in Mason's repo, generate experiment hypotheses, run them, and analyze results; evaluated on the speed of running thoughtful experiments. Flexible scheduling, often run over a weekend. Stage 5 — Offer Extended Stage 6 — Candidate Hired — Candidate accepts and starts.

Ideal Companies & Backgrounds

No Ideal Companies section was present on the role page. Background signals drawn from Green Flags / Nice-to-Haves: Self-driving / AV — Waymo, Zoox Regulated application-layer AI — medical, legal Climate tech — mission-oriented startups