Senior Engineering Manager - AI Geospatial Assistant Team

About the Role:

Planet’s mission is to image the entire world every day, making global change visible, accessible, and actionable. We are at a critical inflection point: operationalizing promising AI research into delivery-focused enterprise "productization". To drive this, we are building a new product group focused on launching an AI Geospatial Assistant that transforms how our customers interact with global imagery to solve high-stakes problems in forensics and daily change detection.

Our goal is to make these complex insights accessible through an intuitive interface that requires zero user training. Operating with a zero-to-one startup mindset, this team prioritizes weekly learning velocity and customer-driven graduation criteria to move rapidly from private alpha to general availability.

As our Senior Engineering Manager, you are tasked with leading the team, with your product partner through this transition. You will lead a high-velocity squad of engineers, transitioning geospatial AI capabilities into a robust, market-ready product. Your focus is on operational excellence, defining success thresholds, managing scope, and ensuring that our bleeding-edge tech graduates into a dependable tool that provides a durable competitive advantage for our customers.

This is not a traditional Engineering Manager role. The EM for this team is expected to be personally AI-native, someone who builds with AI tools daily, thinks actively about how AI is changing what engineering teams look like, and is prepared to pioneer a culture where AI is a core collaborator in the development process, not a tool to be managed cautiously.

This is a full-time, hybrid role which will require you to work from our San Francisco office 3 days per week.

Impact You'll Own:

• Build a High-Density Talent Engine: You will be responsible for the full talent lifecycle—from sourcing and hiring the initial high-agency founding squad to designing an onboarding experience that gets specialists productive in a complex geospatial domain.
• Establish a Culture of Extreme Ownership: Foster an environment where engineers own customer outcomes rather than just technical tasks. You will define what accountability looks like in a high-uncertainty, non-deterministic AI environment.
• Team Operations: Design and iterate on the team’s "operating system" (e.g., sprint cadences, RFC processes, and automated testing) to ensure the shift from research to product is supported by rigorous engineering discipline.
• Collaborate with AI Researchers: Work closely with our AI Research Team to understand their models, workflows, and emerging capabilities, then guide your team in translating research into production ready systems that deliver real value to users. 
• Establish Evaluation-Led Development: Ensure the team builds "Evals" (automated benchmarks) before they build features, ensuring that we measure improvement velocity rather than just raw output.
• Career Architecture & Mentorship: Actively coach and mentor a diverse group of engineers (from Fullstack to Applied AI), navigating their career paths and maintaining high levels of psychological safety and engagement.
• Strategic Roadmapping &am