Autonomy Engineer - Deep Learning Model Acceleration

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

• 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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