Staff Product Manager, Vehicle AI

Skydio•San Mateo, CA

10d•$196,500 - $250,000•Onsite

About The Position

Skydio is seeking a Staff Product Manager for Vehicle AI to shape the future of autonomous flight. In this role, you will own the roadmap for advancing vehicle intelligence, working closely with world-class autonomy teams. You will partner with engineering, design, and customers to define and deliver what autonomous systems should do, how they should interact with humans, and the infrastructure required to continuously improve their capabilities. This role involves shaping the entire lifecycle of autonomous robotics at Skydio, from data collection and training to evaluation and deployment.

Requirements

7+ years building deeply technical products in robotics, AI/ML, autonomous systems, or other complex software domains.
Strong technical fluency and comfort working closely with engineers on system architecture, model performance, and technical tradeoffs.
Experience building products that improve through data, machine learning, or continuous feedback loops.
Experience and aptitude for large scale data analytics skills and ability to generate data-driven insights
Passion for understanding real-world customer workflows and translating them into product strategy.
Excellent judgment in ambiguous environments and the ability to prioritize high-impact opportunities.
Strong ownership mindset and a track record of driving cross-functional initiatives from concept to deployment.
Obtaining FAA Part 107 certification within the first 60 days of employment is strongly encouraged for all Skydio employees and required for certain positions.

Responsibilities

Own the roadmap for advancing Skydio's autonomous capabilities and vehicle intelligence.
Define how autonomous systems should perceive, reason, plan, communicate, and act in real-world environments.
Work with customers and field teams to understand operational workflows and translate them into autonomy requirements.
Drive the end-to-end autonomy development lifecycle, including data collection, labeling, training, simulation, evaluation, deployment, and monitoring.
Define benchmarks, success metrics, and validation frameworks for autonomy performance, safety, and reliability.
Own the user experience of autonomy, including how operators specify goals, understand system intent, and collaborate with autonomous systems.
Partner across machine learning, perception, planning, simulation, data platform, and software engineering teams to deliver new capabilities.
Evaluate and apply emerging technologies, including foundation models, multimodal AI, and agentic systems.