About the job
Skydio stands at the forefront of drone technology as the leading US drone company and the global pioneer in autonomous flight—an essential aspect of the future of aerial mobility. Our team is comprised of experts in artificial intelligence, cutting-edge hardware and software development, operational excellence, and an unwavering commitment to customer satisfaction. We strive to empower a diverse range of drone users, from utility inspectors to first responders and beyond.
About the Role:
Our autonomy system relies on learning both semantic and geometric interpretations of the world through visual data. We are challenging the limits of real-time deep networks to enhance the capabilities of intelligent aerial robots, enabling them to autonomously navigate uncharted environments and deliver significant operational benefits. If you are passionate about applying deep learning in real-world scenarios and tackling complex issues in computer vision and autonomy, we would be thrilled to connect with you.
In the role of Deep Learning Engineer, you will focus on training and deploying optimized models to tackle difficult challenges such as optical flow estimation, stereo depth estimation, object detection, segmentation and tracking, visual place recognition, localization and mapping, few-shot learning, occupancy networks, automated path planning, and more.
Your Impact:
Design, implement, and deploy advanced computer vision and multimodal deep learning models for Skydio's autonomy system.
Utilize extensive real-world video and sensor data for data mining, curation, labeling, training, and evaluation.
Employ large-scale and diverse synthetic data to enhance deep learning algorithms.
Utilize state-of-the-art foundation models for knowledge distillation and efficient learning.
Optimize models for low-latency performance on embedded hardware.
Develop evaluation benchmarks and metrics to assess the performance of autonomous systems.
Act as a versatile team member, contributing across various software aspects as needed.
Qualifications:
M. S. or Ph. D. in Computer Science, Electrical Engineering, or a related field.
Proven hands-on experience in designing, training, and deploying deep learning models.
Ability to produce high-quality, well-architected code (Python/PyTorch preferred).

