Product Manager, TeraWatt Infrastructure

• Bachelor’s or Master’s degree in computer science, engineering, information systems, business administration or related field
• 5+ years experience in a highly cross-functional, fast-paced environment — management consulting, investment banking, product management, or a top-tier operating role
• Demonstrated hands-on AI fluency (e.g., shipped an AI-powered application, or led an AI rollout)
• The ability to go from ambiguous problem to a structured approach and solution fast, even with fragmented operational systems and messy real-world workflows
• Comfort with unstructured environments where you create the playbook, not follow one
• Exceptional written and verbal communication skills; you can speak the language of both engineers and executives (incl. setting metrics/KPIs and reporting on these on a regular cadence)
• Experience influencing senior stakeholders without direct authority and getting cross-functional teams to adopt new tools and processes
• Comfort working closely with engineers — writing specs, debugging in a notebook, reviewing model outputs — even if you don’t write production code

Desirable

• This is Terawatt's product lead for internal AI. Your goal is to make Terawatt AI-native by building the internal operating system
• You will define and execute the strategy for how every Terawatt employee across Development, Finance, Operations, and Technology uses AI to work faster, with better judgment, and at greater scale. You will own the roadmap, ship internal tools, drive adoption, and report impact directly to the C-suite
• This role is focused exclusively on how Terawatt's own teams work — it is distinct from AI applied to our customer-facing CMS or energy products. If you're an operator, builder, or strategist who's been asking yourself "how do I get closer to building something?" — this opportunity is for you
• Lead cross-functional discovery across all Terawatt teams (e.g. Development, Finance, Operations, and Technology) to identify high-leverage pain points and prioritize these by business impact
• Define product requirements, build prototypes using AI tools, gather user feedback, and iterate rapidly — owning the full product lifecycle from zero to deployed
• Scale solutions that gain internal traction by leveraging engineering resources to harden, integrate, and expand proven tools
• Define and report on adoption and impact metrics (hours saved, workflows automated, % active users) to the executive team
• Communicate clearly and compellingly to C-suite stakeholders on the business value delivered
• Run the internal AI intake and prioritization process: source use cases from every function, evaluate ROI, and maintain a transparent roadmap
• Partner with the Technology, Legal, and Security to own responsible-use guardrails for internal AI — including model evaluation, data handling, and security policy
• Lead change management and enablement — training, prompt libraries, internal community — so AI tools are actually used, not just shipped