Autonomy Engineer - Deep Learning Infrastructure
AI summary of the role
Build and scale deep learning infrastructure supporting Skydio's autonomy stack across embedded and cloud environments.
What you’ll do
- Develop high-performance DL inference for CV workloads across hardware platforms
- Profile and optimize CV/VLM performance for throughput, latency, and power efficiency
- Design end-to-end MLOps workflows for model deployment, monitoring, and retraining
- Implement GPU kernels and custom training efficiency methods
What you’ll bring
- Hands-on MLOps, ML inference optimization, and edge deployment experience
- Strong DL fundamentals and state-of-the-art models/architectures
- Deep CV, image/video processing expertise
- Experience building complete ML pipelines for vision tasks
Technologies
deep-learning · MLOps · CV · VLM · GPU-kernels · ML-inference · edge-deployment · vision-language-models · optical-flow · object-detection
About Skydio
Builds autonomous drones, docks, and operating software for public safety, defense, and critical infrastructure teams that need trusted U.S.-made aerial systems.
Series F · 500–1000 people
Source and classification
Internal deployment & tooling · Evidence for this classification:
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
More from the job description
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 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 a [... source excerpt omitted ...] tem 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 building and managing ML pi [... source excerpt omitted ...] es 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 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 Compensation: At Skydio, our compensation packages for regular, full-time empl
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