Autonomy Engineer - Deep Learning Infrastructure

About the role:

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 (CV) such as 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 Deep Learning (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 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 (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 acceleration/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 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

• 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

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