Planet designs, builds, and operates the largest constellation of imaging satellites in history.
Key Responsibilities
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.
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.
Personally AI-native: you write production code with AI tools, have strong opinions on how AI is changing the engineering SDLC, and are prepared to build a culture where AI is a core collaborator, not a compliance consideration
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.
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.
Benefits & Perks
These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.
Comprehensive Medical, Dental, and Vision plans
Health Savings Account (HSA) with a company contribution
Generous Paid Time Off in addition to holidays and company-wide days off
16 Weeks of Paid Parental Leave
Wellness Program and Employee Assistance Program (EAP)
Home Office Reimbursement
Monthly Phone and Internet Reimbursement
Tuition Reimbursement and access to LinkedIn Learning
Equity
Commuter Benefits (if local to an office)
Volunteering Paid Time Off
Ready to Apply?
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