Mason AI — Founding AI Engineer

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