Autonomy Engineer - Deep Learning Infrastructure

Skydio is the leading U.S. 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.

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

• 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 acceleration/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 machine learning.
• Leverage your expertise and best practices to uphold and improve Skydio’s engineering standards.

• Demonstrated hands‑on experience with MLOps, ML inference acceleration/optimization, and edge deployment.
• Strong knowledge of DL fundamentals, techniques, and state‑of‑the‑art DL models/architectures.
• Strong fundamentals in computer vision, image processing, and video processing.
• Demonstrated hands‑on experience building and managing ML pipelines for solving vision or vision‑language tasks including data preparation, model training, model deployment, and monitoring.
• Experience and understanding of security and compliance requirements in ML infrastructure.
• Experience with ML frameworks and libraries.
• Ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, deployment, and monitoring.
• Comfortable navigating and delivering within a complex codebase.
• Strong communication skills and 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.

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