Autonomy Engineer - Deep Learning 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.

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 enterprise and government. Learning a semantic and geometric understanding of the world from visual data is the core of our autonomy system. We are pushing the boundaries of what is possible with real-time deep networks to accelerate progress in intelligent mobile robots.

About the role:

If you are excited about leveraging massive amounts of structured video data to solve problems in Computer Vision (CV) such as object detection and tracking, optical flow estimation and segmentation, we would love to hear from you.

How you'll make an impact: 

As a deep learning infrastructure engineer, you will be responsible for building and scaling the infrastructure that supports Skydio’s DL and AI efforts. You will be working at the nexus of Skydio’s autonomy, embedded and cloud teams to deliver new capabilities and empower the deep learning team.How you’ll make an impact:

- Develop solutions for high-performance deep learning inference for CV workloads that can deliver high throughput and low latency on different hardware platforms

- Profile CV and Vision Language Models (VLMs) to analyze performance, identify bottlenecks and optimization opportunities and improve power efficiency of deep learning inference workloads

- Design and implement end to end MLOps workflows for model deployment, monitoring and re-training

- Utilize advanced Machine Learning knowledge to leverage training or runtime frameworks or model efficiency tools to improve system performance

- Create new methods for improving training efficiency

- Implement GPU kernels for custom architectures and optimized inference

- Design and implement SDKs that allow customers/external developers to create autonomous workflows using ML

- 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 MLOps, ML inference optimization and edge deployment

- Strong knowledge of DL fundamentals, techniques and state-of-the-art DL models/architectures

- Strong fundamentals in CV, image processing and video processing

- Demonstrated hands-on experience buil