Autonomy Engineer - ML & DL Infrastructure

About the Role:

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.

If you are excited about leveraging massive amounts of structured video data to solve open problems in object detection and tracking, optical flow estimation and segmentation, we would love to hear from you. 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.

How You’ll Make an Impact:

• 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

What Makes You a Good Fit:

• 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, and 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