Product Engineer

Remote

As Swing's Product Engineer, you'll find the problems worth solving across our business, decide what to build, and build it, reporting to the VP of Engineering. This isn't a traditional engineering seat: there's no PM >
Designer >
Engineer handoff, and you'll work directly with Product, Operations, Customer Success, Sales, and Finance to take problems from first conversation to shipped software. You'll build full-stack features for school administrators and substitute teachers, the operational systems that run our marketplace, and the automations and AI agents that replace manual work. AI is central to how you work, not just what you ship, and the role is designed so one person with the right tools can do what used to take a team.

If you want to own problems end to end and see your work keep students learning, keep reading.

• Full-stack engineering with a shipping record: You have 3+ years of software engineering or equivalent hands-on product building, and you've shipped real products that customers use. Here you'll work across the stack, from features school administrators and subs use every day to billing, timekeeping, configuration, integrations, and admin tools.
• An AI-native way of working: Tools like Claude Code, n8n, and other AI and automation tools are the core of your workflow, and you keep experimenting to increase your leverage. You'll be expected to turn an idea into something users can interact with in hours or days, not weeks.
• Product judgment: You ask why before deciding how. Without waiting on a PM, you'll define requirements, set scope, make UX and product tradeoffs, and decide when something is ready to ship.
• Turning ambiguity into working software: Problems will reach you loosely defined, from teams across the business. You dig in, work out what's actually needed, and build a solution that works.
• Automating operations: You spot repetitive manual workflows and replace them with software, AI agents, APIs, MCP, or tools like n8n. Hands-on experience building with LLM APIs, agents, or AI-powered workflows is what lets you do this well.
• Clear communication and measurable results: You explain tradeoffs plainly to non-technical teammates and in depth to engineers, collaborating during U.S. business hours. You use data and user feedback to show whether what you built actually solved the problem.
• Founder or early-startup experience is a plus: Having built a product or startup yourself, been an early engineer, or taken on PM responsibilities will help you hit the ground running. So will examples where AI let you accomplish what previously required several people.

You’ll need hands-on experience with React for building user interfaces in JavaScript or Type Script, working with REST APIs to connect systems, and using SQL and relational databases to query and shape data. Those are the core of the stack you’ll touch every day.

Experience with any of these will let you move faster:
Clojure or another Lisp, since much of our backend is Clojure;
Datomic, the database we use for our core application data; AWS infrastructure, where our systems run; or building browser extensions, which we use to extend third-party platforms our customers rely on. If you haven’t worked with these, you’ll pick them up as you go.

• The mission: The average student spends six months of their K-12 career with a substitute teacher. What you build helps schools get coverage quickly and keeps student learning on track.
• End-to-end ownership: You decide what to build, build it, and measure whether it worked. No handoffs, no waiting on a spec.
• A mandate to define AI-native work: We're rethinking how we build products and…