The opportunity

A small, venture-backed San Francisco team is bringing AI into commercial real estate — properly, not as a wrapper. They design, build, and operate bespoke AI agents for the people who run acquisitions, investment management, and asset management. Each agent is trained on an individual firm's own templates and criteria, plugged into the tools those teams already live in, and triggered the instant new work arrives.

They are looking for a product engineer to build the platform alongside them.

Why the seat matters

The team is small enough that nothing is far away from you. You write code daily, work shoulder to shoulder with the CTO on technical direction, and hold equity at founding-engineer level. What you ship reaches paying customers within days, and those customers push real transactions through it.

You would join as the CTO's counterpart and sit inside engineering leadership from the first week. Because the company is early, you help shape not just the architecture but the product and the team — and your remit widens as the business does.

What lands on your desk

• The platform's core architecture, and genuine authority over the technical calls that shape it.
• Pipelines that take chaotic real-world documents and return clean, structured data.
• The infrastructure that keeps AI output accurate enough for institutional clients to rely on.
• Engineering standards, tooling, and review culture — plus a hand in hiring the engineers who will inherit them.
• The genuinely hard problems: making AI output dependable, moving quickly over large financial datasets, and delivering across web, spreadsheets, and email.

What they are looking for

• Systems you built that are still running in production today, and that you are proud of structurally.
• Real depth in either TypeScript or Python — both are in use — and the confidence to work at any layer.
• You have owned a product's architecture, including the tradeoffs and the maintenance bill that follow.
• You have raised the level of a team through review, mentoring, or hiring, rather than through your own output alone.
• Firm footing in databases, distributed systems, and API design.
• The ability to take a fuzzy problem, decide what to build, and deliver it in increments.
• Clear writing, and the ability to explain a technical decision to someone who is not an engineer.

Nice to have

• LLMs, prompt engineering, or AI pipelines running in production.
• Experience wrangling large, messy real-world data.
• Time as a founding or early engineer somewhere.
• Open-source contributions.
• Infrastructure and DevOps background.

Compensation

• Base salary. $200,000 – $250,000.
• Equity. 1% – 5%.

How they work

The team works together in person, Monday to Friday, at their San Francisco office — and they put effort into making the office somewhere people actually want to be:

• Every evening is protected after 6:30pm for an hour or two, so everyone can train and eat a real dinner.
• Meals at the office are covered, alongside a subsidised gym membership and supplements.

The values they name are integrity, competitiveness, and craft. The pitch is straightforward: do the best work of your career next to people who take it as seriously as you do.