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 https://www.skydio.com/solutions/energy-and-utilities to first responders https://www.skydio.com/solutions/public-safety, soldiers in battlefield scenarios https://www.skydio.com/solutions/national-security/tactical-isr, and beyond https://www.skydio.com/solutions.

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

#LI-SM1

At Skydio we believe that diversity drives innovation. We have created a multidisciplinary environment that embraces the power of diverse perspectives to create elegant solutions for complex problems. We are committed to growing our network of people, programs, and resources to nurture an inclusive culture.

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.

For positions located in the United States of America, Skydio, Inc. uses E-Verify to confirm employment eligibility. To learn more about E-Verify, including your rights and responsibilities, please visit https://www.e-verify.gov/