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Autonomy Engineer - Deep Learning Infrastructure

Skydio· R&D
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Hybrid (San Mateo, California, United States)$170k - $236.5kFull-timePosted 6mo ago

About the role

Role focused on building and scaling the infrastructure that supports Skydio’s deep learning and AI efforts, bridging autonomy, embedded, and cloud teams to enable high-performance CV workflows, MLOps, and optimized inference across hardware platforms.

Responsibilities

  • 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 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 ML
  • Leverage your expertise and best-practices to uphold and improve Skydio’s engineering standards

Qualifications

  • Demonstrated hands-on experience with MLOps, ML inference 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
  • Ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, deployment, and monitoring
  • 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

Skills

Required:MLOpsML inference optimizationEdge deploymentCV / Vision processingMLOps pipelines
Nice to have:Security and compliance

Benefits

  • Paid vacation time
  • Sick leave
  • Holiday pay
  • 401K savings plan
  • Group health insurance plans
Last updated Oct 22, 2026. Interested? Apply on the company site.
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