Autonomy Engineer - ML & DL Infrastructure

Skydio is the leading US drone company and the world leader in autonomous flight, the key technology for the future of drones and aerial mobility. The Skydio team combines deep expertise in artificial intelligence, best‑in‑class hardware and software product development, operational excellence, and customer obsession to empower a broader, more diverse audience of drone users, from utility inspectors to first responders, soldiers in battlefield scenarios, and beyond.

Skydio is the leading US drone company and the world leader in autonomous flight. We leverage breakthrough AI to create the world’s most intelligent flying machines for use by our enterprise, public safety, defense and other customers. Learning a semantic and geometric understanding of the world from best‑in‑class visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with deep networks, AI and ML to accelerate progress in intelligent aerial robots that can autonomously navigate in unknown environments and deliver operational value to users.

As a deep learning infrastructure engineer, you will be responsible for building and scaling the infrastructure that supports Skydio’s DL and AI training efforts. You will be working at the nexus of Skydio’s autonomy and cloud teams to deliver new capabilities and empower AI/ML solutions at Skydio.

• Design and implement scalable, extensible, interactive data pipelines and annotation workflows
• Build tools that leverage state‑of‑the‑art machine learning systems for efficient data exploration and curation across the fleet of Skydio drones
• Design and implement pipelines for data ingestion, versioning, model training, deployment and monitoring
• Optimize and scale deep learning training workflows to improve team iteration velocity
• Leverage your expertise and best‑practices to uphold and improve Skydio’s engineering standards

• Demonstrated hands‑on experience with data engineering and building large‑scale, performant and efficient data processing pipelines
• Demonstrated hands‑on experience with cloud‑based ML platforms, containerization technologies, ML Ops platforms and databases
• Experience and understanding of security and compliance requirements in ML infrastructure
• Demonstrated hands‑on experience building and managing ML pipelines including data preparation, model training, model deployment and monitoring
• You have demonstrated ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, deployment, and monitoring
• You are comfortable navigating and delivering within a complex codebase
• Strong communication skills and the ability to collaborate effectively at all levels of technical depth

Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or other characteristics protected by federal, state or local anti‑discrimination laws.