This listing pays about $275,000 a year. Across 30 open ai engineer listings that state pay, the middle half runs $144,500 to $222,500 and the median is $175,000. That is 57% above the median, above the middle half of those listings.

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Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

ABOUT TOWN

Town is building the AI coworker for every team. Everyone gets their own Townie, an AI assistant that learns how you work (your voice, judgment, relationships, and priorities) and does real work on your behalf across email, calendar, docs, Slack, and CRM. Townies don't wait to be prompted. They observe, learn, and act. And because every teammate has one, Townies can ask each other questions and hand off work, so the whole team moves faster together.

Four months after our public launch, we became a $1B company. We recently raised a $108M Series B, co-led by Forerunner Ventures and Menlo Ventures, with continued support from Andreessen Horowitz, First Round Capital and angels such as Zach Perret, Soleio Cuervo, Scott Belsky, and more. Our founders, Jean-Denis and Tony, previously led EPD at Plaid and applied AI product at Google.

ABOUT THE ROLE

Town is building the most personalized, most capable AI assistant for everyone — one that knows you deeply, works across every tool you use, and gets sharper over time. Building the best assistant means proving it's the best: every model, prompt, and system change has to be measurably better, on every surface it touches.

That's what you'll own. You'll build the evals and quality systems that turn assistant performance into numbers the whole team can trust, measuring and improving the full multi-step trajectory the assistant takes to do real work. You'll build the model routing that puts the right model in the right place balancing cost, quality, and speed.

This is a foundational, 0→1 build with ownership to match: the eval framework, the golden datasets and labeling loop, model routing, and online measurement, and you set the bar for what "best" means at Town.

WHAT YOU'LL DO

- Build a generalized eval system that measures assistant quality across every surface it touches — and, crucially, across multi-step agent trajectories.

- Stand up golden datasets and the labeling loop that keeps them up to date and constantly checking to validate improvements and avoid regressions.

- Build model routing and online evaluation tooling to help us learn and route to the best models.

- Make every prompt and system change measurable, so the team can move fast without breaking what works.

- Partner with engineers across the product to instrument quality and close the loop from signal to fix.

YOU MIGHT THRIVE HERE IF YOU...

- Have built or owned LLM eval systems, or offline/online quality measurement at scale.

- Think rigorously about measurement. Maybe that came from an MLE or applied-ML background, maybe not, the instinct for how to measure "better" matters more than the exact pedigree.

- Know the eval landscape hands-on, off-the-shelf tooling and eval frameworks, and have opinions on what to reach for when.

- Are comfortable reasoning about model routing and the tradeoffs between models.

- Ship the fixes, not just the dashboards and metrics.

- Are a senior or staff engineer comfortable in greenfield, where the system doesn't exist yet.

LOCATION

San Francisco, CA. Five days a week in person at our Financial District office.