Senior Autonomy Engineer - Deep Learning

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:

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 solving open-end problems in object detection and tracking, motion prediction, flow estimation, and total scene understanding, while leveraging massive amounts of structured video data, we would love to hear from you.

How you'll make an impact:

• Design and implement deep learning solutions that solve detection, tracking, segmentation, and optical flow estimation tasks in real-time on Skydio drones

• Leverage state-of-the-art methods in unsupervised learning, few shot learning, and foundational models for data efficient ML

• Curate and enhance synthetic data that powers our deep learning algorithms along with massive amounts of structured video data

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

• Characterize and quantify the performance of the vision systems 

• Research and prototype new approaches

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

What makes you a good fit:

• Demonstrated hands-on experience creating and deploying deep learning models

• Experience curating synthetic and real-world image datasets

• Solid software engineering foundation and commitment to writing clean, well-architected code (in Python or C++, preferably both)

• Real experience prototyping, training, optimizing, and deploying deep neural networks

• Ability to read and contextualize scientific papers and literature in computer vision

• Ability to thrive in a fast paced, collaborative, small team environment

Compensation: At Skydio, our compensation packages for regular, full-time employees include competitive base salaries, equity in the form of stock options, and comprehensive benefits packages. Compensation will vary based on factors, including skill level, proficiencies, transferable knowledge, and experience. Relocation assistance may also be provided for eligible roles. The annual base salary range for this position is $170,000 - 277,500*. Fundamentally, we believe that equity is the key to long-term financial growth, and we ensure all regular, full-time employees have the opportunity to significantly benefit from the company's success. Regular, full-time employees are eligible to enroll in the Company’s group health insurance plans. Regular, full-time employees are eligible to receive the following benefits: Paid vacation time, sick leave, holiday pay and 401K savings plan. This position and all associated benefits are subject to applicable federal, state, and local laws, as well as the Company’s policies and eligibility criteria.

* Compensation for certain positions may vary based on the position’s location.

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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/