Perception Engineer

Industry: Technology
Remote
Company size: 1-10
Full-time
salary
Not disclosed

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Job description

A fast-growing company that is re-envisioning safety using 3D technology for long-range autonomous vehicles using untethered camera arrays, is looking for a Perception Engineer. The selected candidate will be responsible for building compelling and highly performant applications. The VC-backed company is determined to develop the best real-time perception solutions for autonomous vehicles in the world. This will be a full-time position and will be a great opportunity for developers to work in a fast-paced, challenging environment.

  

Job Responsibilities:

  • Power autonomous vehicle systems by designing, developing, and testing perception algorithms 
  • Deploy algorithms to target platforms, and optimize them to perform within platform constraints
  • Create, improve and maintain datasets for algorithm development
  • Develop and support tools that accelerate development and deployment
  • Take ownership for design, development, deployment, and test

Job Requirements:

  • BS, MS, or Ph.D. in computer science, electrical engineering, mechanical engineering, math, physics, or related field. MS and above preferred
  • At least 3+ years of relevant experience as a software engineer
  • Experience with object detection and segmentation methods, as well as machine learning, deep learning, and computer vision algorithms
  • Read, understand, and implement published neural network architectures
  • Experience with creating, annotating, and updating large datasets for algorithm development
  • Working knowledge of common autonomy sensing technologies, such as camera and Lidar
  • Experience with OpenCV and point cloud processing techniques
  • Experience working with writing clean, well-tested Python and C++ code that is efficient and correct
  • Experience working with production software and cloud development
  • Communicates clearly and effectively
  • Strong 3D math and linear algebra skills
  • Familiarity with optimization of neural networks for deployment

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