AI Product Engineer

This is framed as a Software Engineer but is really a product engineer on an AI product: a strong, AI-native engineer who thinks from the customer's lens, moves fast, and ships features end to end front-to-back. They collaborate constantly with FDEs (who carry all the client feedback) to mesh customer demands into product and build it out — and they're expected to challenge the FDEs ("are you sure the customer wants this, or do they just want the outcome?").

Crucially, it's product lens on top of real engineering depth, not one or the other. But the day-to-day, once your AI-assisted engineering setup is dialed in, becomes product-level scoping — because you've already codified your system-design knowledge into skills/agents that handle verification, edge-case testing, and the like. The archetype is a "cracked," AI-native engineer whose higher slope beats a more-experienced engineer still working the old way.

What You'll Do

• Drive end-to-end design and implementation: task break-down, delivery, testing, monitoring, maintenance
• Own features front-to-back; ship high-quality, simple, elegant designs others can build on
• Collaborate with product, design, FDEs, and engineering to turn specs (and raw client feedback) into product
• Fill gaps on customer-critical initiatives (e.g., Deal View sprints), pairing with FDEs, while owning your own projects
• Influence platform direction with your own ideas; balance immediate business objectives against long-term architecture
• Run the full SDLC and support production use of what you own

Requirements

• 2+ years industry experience as a Software Engineer (sweet spot ~4 years; less is fine if the slope is high — a "cracked" 2-years-out engineer beats a stuck 6-year one)
• Fluency in an AI-forward SDLC — uses LLMs/agents to move fast without sacrificing production-grade quality (has a real workflow: parallel agents, one checks another, skills codified into the build process). This "sparkle in the eye" about their setup is a core signal.
• Experience with: distributed systems / infra & scaling, LLM harnesses / agentic systems, concurrent or async processing
• Strong software-design fundamentals (SOLID or equivalent) and when to apply them; a real system-design bar (dedicated round)
• Testing, monitoring, and reliability built into how they write code
• Comfortable owning a feature end-to-end, front-to-back

Execution and Ownership

• Product lens — empathizes with the customer even without direct client contact; can put a design/UX hat on for sanity checks (there's a designer for polish, so no design/UX background needed)
• High autonomy — takes an ambiguous problem to a well-reasoned design and shipped solution with minimal hand-holding
• Scrappy, gets things done, whiteboards ideas, adapts and jumps across projects to provide value fast
• Ultra-bought-in "along for the ride" energy — sees a product flaw as "we're so close, one tweak and they'll love it," not "this sucks"; fire/grit is a big CEO-round filter
• A genuinely good person to work with 9-to-9 (culture-indexed, though pure technical brilliance can outweigh it)

Background

• Startup experience welcomed (scrappy, loosely-defined, owns top-to-bottom); ex-founders a strong signal — even a failed company is a plus if the "why it failed" answer shows grit and self-awareness
• AI keyword depth on paper: LLM evals / eval harnesses, RAG, vector DBs, agentic/cutting-edge hands-on work

Who Will Thrive Here

• A cracked, AI-native product engineer who ships end to end and thinks customer-first
• A high-slope builder with a dialed-in agent workflow who moves fast without dropping quality
• Someone who owns their own projects and jumps on customer-critical sprints with the FDEs
• Ultra-bought-in, gritty, "take it to the moon" energy; a good person for a 9-to-9 team
• An ex-founder / scrappy startup builder who owns problems top to bottom