Senior Engineering Manager - AI Geospatial Assistant Team

Planet•San Francisco, CA

5h•Hybrid

About The Position

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

Requirements

6+ years of relevant experience
4+ years of experience leading software engineering teams
Bachelor’s degree in a relevant field
Track record of hiring and leading founding engineering teams in a startup or high-growth "intrapreneurial" environment.
Experience and enthusiasm for leading AI engineering teams that have successfully elevated research prototypes into production-grade applications. This enthusiasm should not only inform the product features, but should also foster a culture of developer workflows that take an AI-assisted development approach
Expert in the "human element" of engineering—managing conflict, delivering difficult feedback, and keeping a team motivated through the inevitable pivots of an early-stage product.
Ability to identify "process bugs" (why a team is slowing down) and implement the cultural or structural changes needed to restore velocity.
Technical Breadth: While you may not be the deepest specialist on the team, you have the breadth to synthesize input from AI researchers and frontend experts to make sound architectural and personnel decisions. This includes an ability to identify optimizations that may be better handled with a collaboration with a research team (eg. request development of a fine tuned model for a slow but simple LLM call)
Pragmatic Execution: A "good enough—move on" mentality that avoids seeking perfection at the cost of delivering meaningful customer value.
Impact focused: You are excited by building an application that once launched, will give researchers, journalists, governments, and NGOs the ability to explore the world using natural language and surface crucial insights that used to take months to find.
Experience with geospatial data or planetary-scale analytics is highly preferred.

Nice To Haves

LLM Observability and Monitoring: Prior experience with tools and techniques for managing non-deterministic systems (e.g., LangSmith, Arize, or similar).
Public Leadership: You are comfortable representing the team’s work to executive leadership and external stakeholders, acting as a "shield" for your team so they can focus on building.
Product-First Mindset: A track record of balancing technical excellence with the urgency of market delivery and "graduation criteria".
Geospatial Context: Prior experience with geospatial data, satellite imagery, or planetary-scale analytics.

Responsibilities

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 & De-risking: Partner with Product and Design to translate the ai.planet.com vision into a realistic technical roadmap, identifying "long-pole" technical risks early to keep the team unblocked.
Cross-Functional Leadership: Act as a "board of directors" member alongside Product and Design partners, ensuring engineering efforts are laser-focused on forensics and change-detection use cases.