Autonomy Engineer - Deep Learning Infrastructure

Quality Control Specialist - Pest Control; San Mateo (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

A leading technology company is seeking a deep learning infrastructure engineer to join their innovative team. This role involves developing high-performance solutions for computer vision workloads, managing ML pipelines, and collaborating across teams to enhance their deep learning capabilities. Ideal candidates will have strong experience in MLOps, deep learning, and effective communication skills to navigate complex technical projects.

Qualifications

• Hands-on experience with MLOps and ML optimization.
• Strong knowledge of deep learning models and computer vision fundamentals.
• Experience in managing ML pipelines and understanding compliance.

Responsibilities

• Develop high-performance deep learning inference solutions.
• Design and implement MLOps workflows for model deployment.
• Profile CV and VLMs for performance optimization.

Skills

Machine Learning

Computer Vision

Deep Learning

MLOps

Image Processing

Video Processing

Performance Optimization

Collaboration

Tools

ML Frameworks

Inference Optimization Tools

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