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:
Skydio is the leading US drone company and the world leader in autonomous flight. 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 deep networks, AI and ML to accelerate progress in intelligent aerial robots that can autonomously navigate in unknown environments and deliver operational value to users.
If you are excited about leveraging massive amounts of structured video data to solve open problems in object detection and tracking, optical flow estimation and segmentation, we would love to hear from you. As a deep learning infrastructure engineer, you will be responsible for building and scaling the infrastructure that supports Skydio’s DL and AI training efforts. You will be working at the nexus of Skydio’s autonomy and cloud teams to deliver new capabilities and empower AI/ML solutions at Skydio.
How You’ll Make an Impact:
Design and implement scalable, extensible, interactive data pipelines and annotation workflows
Build tools that leverage state-of-the-art machine learning systems for efficient data exploration and curation across the fleet of Skydio drones
Design and implement pipelines for data ingestion, versioning, model training, deployment and monitoring
Optimize and scale deep learning training workflows to improve team iteration velocity
Leverage your expertise and best-practices to uphold and improve Skydio’s engineering standards
What Makes You a Good Fit:
Demonstrated hands-on experience with data engineering and building large scale, performant and efficient data processing pipelines
Demonstrated hands-on experience with cloud-based ML platforms, containerization technologies, ML Ops platforms and databases
Experience and understanding of security and compliance requirements in ML infrastructure
Demonstrated hands-on experience building and managing ML pipelines including data preparation, model training, model deployment and monitoring
You have demonstrated ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, and deployment, and monitoring
You are comfortable navigating and delivering within a complex codebase
Strong communication skills and the ability to collaborate effectively at all levels of technical depth
Obtaining FAA Part 107 certification within the first 60 days of employment is strongly encouraged for all Skydio employees and required for certain positions.
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/
Skills Required
- Hands-on experience with data engineering and building large scale data processing pipelines
- Hands-on experience with cloud-based ML platforms and containerization technologies
- Experience with ML Ops platforms and databases
- Hands-on experience building and managing ML pipelines including model training and deployment
- Ability to drive concepts through the software lifecycle
- Strong communication skills and ability to collaborate
What We Do
Skydio is the leading US drone company and world leader in autonomous flight, the key technology for the future of drones and aerial transportation. Our mission is to make the world more productive, creative, and safe with autonomous flight.
Why Work With Us
The Skydio team combines deep expertise in artificial intelligence, best-in-class hardware and software product development, and operational excellence to empower a broader, more diverse audience of drone users - from action sports enthusiasts to first responders to insurance claims adjusters.
