Skydio
Drones
AutonomyEngineer-DeepLearningInfrastructure
Neural analysis suggests this role is
optimal for Mid candidates.
“Autonomy Engineer - Deep Learning Infrastructure at Skydio. Skills: Deep Learning Infrastructure, MLOps, ML inference acceleration, CV workloads. Develop solutions for high-performance deep learning inference. Profile CV and Vision Language Models”
What You'll Achieve.
deliver new capabilities; empower the deep learning team; deliver high throughput; low latency; improve system performance; improve power efficiency
Industry & Context.
solve problems in Computer Vision; identify bottlenecks; acceleration/optimization opportunities
What They're Looking For.
Must Have
ML inference acceleration/optimization, edge deployment, DL fundamentals, DL techniques, state-of-the-art DL models/architectures, CV fundamentals, image processing, video processing, ML pipelines, data preparation, model training, model deployment, monitoring, security and compliance requirements in ML infrastructure, ML frameworks, ML libraries, software lifecycle, architecture, development, testing, deployment, monitoring, complex codebase navigation
Nice to Have
experience with Python and machine learning frameworks, knowledge of object detection, knowledge of tracking, knowledge of optical flow estimation, knowledge of segmentation
What You'll Do.
Develop solutions for high-performance deep learning inference
Profile CV and Vision Language Models
Design and implement MLOps workflows
Utilize advanced Machine Learning knowledge
Create new methods for improving training efficiency
Implement GPU kernels for custom architectures
Design and implement SDKs
Uphold and improve engineering standards
How You'll Work.
Team & Collaboration
working at the nexus of autonomy, embedded and cloud teams; collaborate effectively at all levels of technical depth; multidisciplinary environment; diverse perspectives
Communication Scope
communication skills; ability to collaborate effectively
Process & Methodology
drive concept through software lifecycle
Full 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 https://www.skydio.com/solutions/energy-and-utilities to first responders https://www.skydio.com/solutions/public-safety, soldiers in battlefield scenarios https://www.skydio.com/solutions/national-security/tactical-isr, and beyond https://www.skydio.com/solutions. 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 Lear
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