Product Engineer

Description

About the company

The company builds industrial AI robots that are working in the real world today. Our robots automate mission-critical warehouse labor end-to-end, starting with e-commerce order packaging. We deploy fast, learn from real production data, and iterate aggressively. We already have a quickly growing number of revenue-generating robots operating in U.S. warehouses, and are now scaling towards thousands of deployments over the next few years. Our mission is simple: build the world's most useful and deployable robot.

WARNING: Robotics is not for the faint of heart. There are no silver bullets here. It is tiresome, unglamorous, and at times brutal work. The only way out is through, and the only way through is treacherous and unknown. Success will require teamwork - so we are building a world-class team to tackle some of the most exciting problems out there. We hope you join us.

The company is looking for a Product Engineer, AI Enablement to build and roll out the tools that help the company use AI effectively across the company. You'll work with engineering, operations, deployment, data, people, and other teams to understand how people work, find where AI creates real leverage, and turn that into reliable internal tools and workflows.

You'll sit on the Software team next to infrastructure and developer productivity. Your users are your colleagues. Half the leverage is how we write and ship software — coding agents, review, testing, repo context, CI. Half is how the rest of the company gets work done — agents and automations people trust. Across both, you'll make the good path easy to reuse, so teams can build their own workflows without waiting on you for every request.

This is a production software role. You will evaluate commercial tools, compose what works, build what doesn't exist, measure adoption, and kill what doesn't earn its keep. This is an in-person NYC role with direct line of sight to how the company builds and operates.

Responsibilities

• Own AI tooling adoption across the company: find where AI improves velocity, build or integrate the solution, teach people to use it, and drive adoption until it sticks
• Build the internal AI stack: services, workflows, interfaces, model integrations, and the agent infrastructure behind them
• Make agents competent and trustworthy in the company's codebase and systems — context, tool access, review, testing, CI — so the good path is the easy path
• Partner with operations, deployment, data, and business teams on workflows that give them leverage, then hand off maintainable systems with clear owners
• Build the reusable layer: connectors, shared context, templates, examples, and guardrails so other people can extend what you ship
• Evaluate the AI tooling ecosystem and decide what the company should adopt, compose, or build
• Partner with infrastructure and security so AI tools have useful access without becoming a liability
• Measure adoption, productivity, and whether the tool is actually trusted; expand what works and kill what doesn't
• Bonus Points
• You're a former startup founder
• You’ve built self-improving, agentic systems in the past that eventually ran without you in the loop
• You have a strong product sense and know all about UX in HIL systems
• You've redesigned a business workflow and measured the gain, not just automated the existing mess
• Expected Compensation
• $150,000 - $225,000/annual salary + equity + benefits
• This compensation range may be inclusive of several career levels at the company and will be narrowed during the interview process based on the candidate’s experience and qualifications. Adjustments outside of this range may be considered for candidates whose qualifications significantly differ from those outlined in the job description.

Requirements

• You have strong software engineering fundamentals and you ship quickly — backend services, internal tools, integrations, automation, and interfaces (Python and/or TypeScript)
• You've shipped agentic systems or LLM-powered tools that real people used, and you can explain where they fail
• You're fluent with modern AI coding tools and you have strong opinions about how they should be used
• You know when to build, when to buy, and when a conventional script beats an agent
• You treat evaluations, permissions, secrets, and safe rollout as part of the product — not a follow-up
• You like working with people who aren't engineers; you can turn an ambiguous workflow into something scoped and useful
• You write clearly and you're comfortable driving adoption, not just writing code
• 4+ years of production software is a reasonable bar; evidence you've shipped this kind of system matters more than the year count