Autonomy Engineer - Deep Learning Model Acceleration

Skydio Skydio · Defense · Zurich, Switzerland · R&D

Skydio is seeking a Deep Learning Model Acceleration Engineer to build and scale infrastructure for their AI efforts, focusing on high-performance deep learning inference for computer vision workloads on various hardware platforms. The role involves profiling models, optimizing for low latency and power efficiency, designing MLOps workflows, and implementing GPU kernels for custom architectures.

What you'd actually do

  1. Develop solutions for high-performance deep learning inference for CV workloads that can deliver high throughput and low latency on different hardware platforms
  2. 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
  3. Design and implement end to end MLOps workflows for model deployment, monitoring, and re-training
  4. Utilize advanced Machine Learning knowledge to leverage training or runtime frameworks or model efficiency tools to improve system performance
  5. Create new methods for improving training efficiency

Skills

Required

  • MLOps
  • ML inference acceleration/optimization
  • edge deployment
  • DL fundamentals
  • CV fundamentals
  • image processing
  • video processing
  • ML pipelines
  • model training
  • model deployment
  • monitoring
  • ML frameworks and libraries
  • software lifecycle management
  • complex codebase navigation
  • collaboration skills

Nice to have

  • Vision Language Models (VLMs)
  • GPU kernels for custom architectures
  • SDK development for autonomous workflows

What the JD emphasized

  • ML inference acceleration/optimization
  • edge deployment
  • ML pipelines for solving vision or vision language tasks
  • security and compliance requirements in ML infrastructure

Other signals

  • Deep Learning Model Acceleration
  • high-performance deep learning inference
  • MLOps workflows for model deployment
  • edge deployment