PhD intern role focused on developing and deploying reinforcement learning policies for autonomous drone planning, navigation, and control in complex environments. Involves research in sim-to-real transfer, human-in-the-loop control, and multi-agent coordination, with a strong emphasis on real-world flight testing.

The role focuses on designing, implementing, and deploying deep learning models for autonomous flight systems, with a specific emphasis on computational photography applications. It involves training and optimizing models for both embedded hardware and cloud deployment, using real-world and synthetic data, and developing evaluation benchmarks.

The role focuses on building and scaling the infrastructure for Skydio's Deep Learning (DL) and AI efforts, specifically for computer vision (CV) workloads. This includes developing high-performance inference solutions, profiling models, designing MLOps workflows, improving training efficiency, implementing GPU kernels, and creating SDKs for autonomous workflows. The role operates at the intersection of autonomy, embedded, and cloud teams, emphasizing ML inference acceleration, optimization, and edge deployment.

The Autonomy Engineer - Deep Learning Model Acceleration role at Skydio focuses on building and scaling infrastructure for AI/ML efforts, specifically optimizing deep learning inference for computer vision workloads on various hardware platforms. This involves profiling models, designing MLOps workflows, improving training efficiency, implementing GPU kernels, and creating SDKs for autonomous workflows, with a strong emphasis on edge deployment and performance optimization.