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

About the role:

We leverage breakthrough AI to create the world’s most intelligent flying machines for use by our enterprise, public safety, defense and other customers. Learning a semantic and geometric understanding of the world from best-in-class 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 aerial robots that can autonomously navigate in unknown environments and deliver operational value to users. Capture and processing of high quality visual data powered by computational photography models and algorithms are key to enabling enhanced camera capabilities and downstream applications such as inspection and mapping, ISR (intelligence, surveillance and reconnaissance) and public safety.

If you are excited about real world applications of deep learning and solving difficult problems in imaging, computer vision and autonomy, we would love to hear from you. As a Deep Learning Engineer Intern, you will be responsible for training and deploying optimized models to our products for solving challenging problems such as multimodal scene understanding, computational photography, optical flow and range estimation, object detection, segmentation and tracking, visual localization and mapping, few-shot learning, automated path planning etc…

How you'll make an impact:

• Design, implement and deploy deep learning models with a particular focus on computational photography applications such as super-resolution, multi-frame denoising, low light imaging, High Dynamic Range (HDR) imaging etc.

• Leverage massive amounts of real world video and other sensor data for data mining, curation, labeling, training and evaluation

• Leverage large scale and diverse synthetic data to power deep learning algorithms

• Leverage state-of-the-art foundation models for knowledge distillation and label efficient learning

• Refine and optimize models for low-latency on embedded hardware

• Refine and optimize models for cloud-based deployment in latency tolerant applications

• Develop evaluation benchmarks and metrics to quantify the performance of autonomous systems

• Be a generalist helping out on all aspects of the software when needed

What makes you a good fit:

• M.S. or Ph.D. in computer science, electrical engineering or related discipline

• Demonstrated hands-on experience designing, training and deploying deep learning models

• Ability to deliver high quality, well-architected code (Python/PyTorch and preferably, C++)

• Leverage state-of-the-art academic papers and literature for fast iteration

• Ability to thrive in a fast paced, collaborative and highly technical team environment

• Comfortable navigating and delivering within a complex codebase

• Strong communication skills

At Skydio we believe that diversity drives innovation. We have created a multidisciplinary environment that embraces the power of diverse perspectives to create elegant solutions for complex problems. We are committed to growing our network of people, programs, and resources to nurture an inclusive culture.

Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or other characteristics protected by federal, state or local anti-discrimination laws.

For positions located in the United States of America, Skydio, Inc. uses E-Verify to confirm employment eligibility. To learn more about E-Verify, including your rights and responsibilities, please visit https://www.e-verify.gov/