Autonomy Engineer - Deep Learning Model Acceleration

Skydio; San Mateo (CA); On-site; USD 170,000 - 277,500; Full time

Benefits offered by this job

Competitive base salary

Equity options

Comprehensive benefits packages

Relocation assistance

Job summary

A pioneering drone technology company in California is seeking a Deep Learning Infrastructure Engineer to enhance its AI capabilities. You will develop high-performance solutions for computer vision workloads and implement MLOps workflows. The ideal candidate has expertise in ML inference acceleration and image processing. Competitive salary ranges from $170,000 to $277,500, with relocation assistance available. Skydio is committed to fostering an inclusive work environment.

Qualifications

• Hands-on experience with MLOps and edge deployment.
• Strong understanding of ML inference and optimization techniques.
• Building and managing ML pipelines for vision tasks.
• Ability to navigate a complex codebase.
• Strong communication skills for effective collaboration.

Responsibilities

• Develop high-performance deep learning inference solutions.
• Profile CV and VLMs for performance and bottlenecks.
• Design and implement MLOps workflows.
• Create methods for improving training efficiency.
• Implement GPU kernels for optimized inference.
• Design SDKs for developers to create autonomous workflows.

Skills

MLOps

Deep Learning fundamentals

Computer Vision

ML frameworks and libraries

Image processing

Video processing

Effective communication

Tools

GPU kernels

Deep Learning inference acceleration

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

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

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*.

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

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/