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Autonomy Engineer - Deep Learning Model Acceleration

Skydio· R&D
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Hybrid (Zurich, Switzerland)Full-timePosted 5mo ago

About the role

Role focused on building and scaling infrastructure for Skydio’s Deep Learning and AI efforts, working at the nexus of autonomy, embedded, and cloud teams to deliver new capabilities for real-time DL inference, model optimization, and end-to-end ML workflows.

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 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

Qualifications

  • 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
  • Demonstrated ability to drive concepts through the software lifecycle: architecture, development, testing, deployment, and monitoring
  • Ability to navigate and contribute within a complex codebase
  • Strong communication skills and collaboration abilities at all technical levels

Skills

Required:MLOpsML inference optimizationCV / computer vision

Benefits

  • (Not specified in the description)
Last updated Jul 16, 2026. Interested? Apply on the company site.
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