Autonomy Engineer - Deep Learning Infrastructure

Autonomy Engineer – Deep Learning Infrastructure

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
Demonstrated ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, and deployment, and monitoring
Comfortable navigating and delivering within a complex codebase
Strong communication skills and the ability to collaborate effectively at all levels of technical depth

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

No preferred qualifications provided.