Autonomy Engineer - Deep Learning Model Acceleration

Skydio; San Mateo (CA); On-site; USD 170,000 - 278,000; Full time

Job summary

Join an innovative company leading the way in drone technology! As a Deep Learning Infrastructure Engineer, you'll build and scale infrastructure that powers cutting-edge AI and deep learning efforts. This role offers the opportunity to work on high-performance solutions for computer vision tasks, optimizing machine learning models, and implementing MLOps workflows. If you're passionate about advancing autonomous flight technology and enjoy collaborating with diverse teams, this position is perfect for you. Embrace the challenge and make a significant impact in the future of aerial mobility!

Qualifications

• Hands-on experience with MLOps and ML inference optimization.
• Strong knowledge of deep learning models and architectures.

Responsibilities

• Develop high-performance deep learning solutions for computer vision.
• Design end-to-end MLOps workflows for model deployment and monitoring.

Skills

MLOps

Deep Learning Fundamentals

Computer Vision

Image Processing

Video Processing

Machine Learning Pipelines

Communication Skills

Education

Bachelor's Degree in Computer Science or related field

Master's Degree in AI or ML (preferred)

Tools

ML Frameworks and Libraries

GPU Kernels

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 inspectorstofirst responders,soldiers in battlefield scenariosandbeyond.

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 leveraging massive amounts of structured video data to solve problems in Computer Vision (CV) such as 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 Deep Learning (DL) and AI efforts. You will be working at the nexus of Skydio’s autonomy, embedded and cloud teams to deliver new capabilities and empower the deep learning team.

How you’ll make an impact:

• Develop solutions for high-performance deep learning inference for CV workloads that can deliver high throughput and low latency on different hardware platforms
• Profile CV and Vision Language Models (VLMs) to analyze performance, identify bottlenecks and acceleration/optimization opportunities and improve power efficiency of deep learning inference workloads
• Design and implement end to end MLOps workflows for model deployment, monitoring, and re-training
• Utilize advanced Machine Learning knowledge to leverage training or runtime frameworks or model efficiency tools to improve system performance
• Create new methods for improving training efficiency
• Implement GPU kernels for custom architectures and optimized inference
• Design and implement SDKs that allow customers/external developers to create autonomous workflows using Machine Learning (ML)
• 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 MLOps, ML inference acceleration/optimization, and edge deployment
• Strong knowledge of DL fundamentals, techniques, and state-of-the-art DL models/architectures
• Strong fundamentals in CV, image processing, and video processing
• Demonstrated hands-on experience building and managing ML pipelines for solving vision or vision language tasks including data preparation, model training, model deployment, and monitoring
• Experience and understanding of security and compliance requirements in ML infrastructure
• Experience with ML frameworks and libraries
• 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

Compensation Range:The annual base salary range for this position is $170,000 - 277,500*. Compensation will vary based on factors, including skill level, proficiencies, transferable knowledge, and experience. In addition to base salary, Skydio full-time employees are eligible to enroll in our benefit plans and take advantage of a variety of incentives and stipends.

*For some positions the pay may be dependent upon the individual's regional location.

#LI-PG

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 veteranstatus, 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/