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SkydioSkydioSan Mateo, CA

Autonomy Engineer - Deep Learning

Designs, trains, and deploys deep learning models for drone autonomy, focusing on computer vision tasks like optical flow, depth estimation, detection, and path planning using real-world and synthetic data. Requires MS/PhD, hands-on DL experience, and PyTorch/Python/C++ proficiency.

170k – 278k/yr
HybridML Engineering

About the role

How you'll make an impact

  • Design, implement, and deploy computer vision and multimodal deep learning models for Skydio’s autonomy system
  • Leverage massive amounts of real world video and other sensor data for data mining, curation, labeling, training and evaluation
  • Leverage large scale and diverse synthetic data to power deep learning algorithms
  • Leverage state-of-the-art foundation models for knowledge distillation and label efficient learning
  • Refine and optimize models for low-latency on embedded hardware
  • Develop evaluation benchmarks and metrics to quantify the performance of autonomous systems
  • Be a generalist helping out on all aspects of the software when needed

What makes you a good fit

  • M.S. or Ph.D. in computer science, electrical engineering or related discipline
  • Demonstrated hands-on experience designing, training and deploying deep learning models
  • Ability to deliver high quality, well-architected code (Python/PyTorch and preferably, C++)
  • Leverage state-of-the-art academic papers and literature for fast iteration
  • Ability to thrive in a fast paced, collaborative and highly technical team environment
  • Comfortable navigating and delivering within a complex codebase
  • Strong communication skills

Compensation

Annual base salary range: $170,000 - $277,500. Includes equity (stock options), comprehensive benefits (health insurance, paid vacation, sick leave, holidays, 401K). Relocation assistance may be provided.

Skills

PyTorchPythonC++Deep LearningComputer VisionOptical FlowStereo Depth EstimationObject DetectionSegmentationTrackingVisual Place RecognitionLocalizationMappingFew-Shot LearningOccupancy Networks

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