AI Native Technical Product Manager

We are seeking an AI Native Technical Product Manager to lead our customer-facing software products - the Customer Platform and other tools our wind energy customers use every day. Your goal is simple to state and hard to achieve: deliver products that are actually used. You will be measured on engagement, adoption, and market value - not on features shipped or documents written. This is an AI-first product role: you will prototype with Claude Code and other coding agents, write specifications that agents can execute, build simple features yourself, and drive an agentic, specification-first development process together with our software teams. You don't need to be a career engineer - but you must be someone who builds constantly with AI, understands where AI-native product development is heading, and pulls the whole team in that direction. If you're excited to define what product management looks like when software is cheap and judgment is scarce, in a fast-growing robotics company, we encourage you to apply.

Key Responsibilities and Duties: •         Own Product Outcomes: Define, instrument, and own the metrics that matter - user adoption, engagement, retention, and commercial value - for our customer-facing products. Success is wind farm operators using the product weekly, not a roadmap item marked done. •         Product Strategy & Discovery: Run continuous discovery with customers (wind farm operators, OEMs, ISPs), translate insights into a clear product strategy and roadmap, and make ruthless prioritization calls under uncertainty. Create clarity in the ambiguity that rapid AI progress produces. •         Prototype-First Validation: Turn ideas into working prototypes with AI coding agents before committing engineering capacity. Send your spec to Claude Code and see what comes back - validate UX and feasibility with clickable, working software, not slideware. •         Agentic Development Process: Lead a specification-first, AI-augmented delivery workflow with the engineering team. Write agent-executable specs with explicit acceptance criteria, maintain the product context layer (PRDs, CLAUDE.md files, domain documentation) that makes coding agents effective, and treat every model release as a prompt to revisit what's possible. •         Hands-on Delivery: Develop and ship simple features end-to-end using coding agents - copy changes, UI tweaks, small workflow improvements, internal tools - without blocking an engineer. Read code, review agent output, and understand the systems you manage. •         Evals & AI Feature Quality: Define success measures and evaluation sets for AI-powered features (damage detection, analytics, agentic workflows). Ensure AI features are verified against real customer scenarios, not just demos. •         Team Enablement: Encourage and coach the team in AI-native ways of working - shared prompt libraries, agent workflows, demos, and standards. Raise the bar for how the whole product organization uses AI. •         Go-to-Market & Stakeholders: Partner with commercial, operations, and leadership to launch products, drive adoption inside customer accounts, and communicate strategy convincingly to both executives and engineers.