AI Product Engineer

Attention is transforming how go-to-market teams operate by turning every customer interaction into structured data, insights, and intelligent action. Their AI agents help organizations execute faster, learn continuously, and make every deal cycle more efficient and predictable. Trusted by teams at Scale AI, Lovable, BambooHR, Elise AI, Abridge, and hundreds of other rapidly growing companies. The belief is that the best AI products feel deeply human-centered, rather than replacing sales reps with agents, they empower them to answer critical questions while automating manual busywork: which deals need attention right now, how much revenue is expected this quarter, what automations have run and whether they're correct.

The engineering team started remote with strong presence in Colombia, and is now building out the New York presence, a tiger team of really good product engineers. Currently ~30 developers with only 3 ever lost. Direct access to the CPO, super collaborative flat structure, heavy investment in internal tooling and dev efficiency. Multiple funding rounds raised, expanding quickly. Series B, about 51 to 200 people, founded in 2021.

This seat is posted as a software engineer, but it is a product engineer on an AI product. You think from the customer's lens, move fast, and ship features end to end, front to back. You work constantly with field engineers who carry client feedback, turn that into product, and push back when the ask is a feature instead of the outcome the customer actually wants. Product sense sits on top of real engineering depth, not instead of it.

You drive design and implementation: break down the work, deliver, test, monitor, and keep it healthy in production. You own features other people can build on, fill gaps on customer-critical work (Deal View sprints and the like) while still owning your own projects, and weigh near-term business needs against long-term architecture.

Bar: 2+ years as a software engineer (about 4 is the sweet spot; less is fine if you move fast). Fluency in an AI-forward way of building, a real agent workflow, parallel agents, skills baked into how you ship, without dropping production quality. Experience with distributed systems and scaling, LLM harnesses or agentic systems, and concurrent or async work. Strong software design and a real system-design bar. Testing, monitoring, and reliability are how you write code, not a later pass. You are comfortable owning a feature front to back.

High autonomy: take an ambiguous problem to a reasoned design and a shipped solution. Scrappy, whiteboard, jump across projects when it helps. Startup or ex-founder ownership is a plus. Hands-on LLM evals, RAG, vector databases, or agent systems on paper should match what you can talk through. This is onsite in New York. Visa sponsorship is available. Equity is about 0.05% to 0.2%. Health, dental, vision, and new equipment and software.