AI / Computer Vision (ic)

Samsara Samsara · Enterprise · San Francisco, CA · Remote · Platform

Senior Individual Contributor role on the Safety AI team, focused on taking computer vision models from research to production in safety-critical systems. The role involves architecting end-to-end CV pipelines, optimizing models for edge inference on constrained devices, and collaborating with hardware teams. The work impacts industries that run the world, ensuring safety and efficiency in physical operations.

What you'd actually do

  1. Architect end-to-end computer vision pipelines for real-world safety detection — object detection, tracking, semantic segmentation, multi-camera fusion, and beyond
  2. Drive the technical roadmap for edge and cloud perception, including how we optimize and deploy models on constrained hardware without sacrificing accuracy
  3. Partner closely with our hardware and firmware teams — we build our own devices, which means you'll have a rare ability to co-design the full stack
  4. Work with petabyte-scale multimodal data (video, sensor, telematics, diagnostics) to train and iterate on production models
  5. Stay at the frontier of CV and perception research and translate what matters into shipped product

Skills

Required

  • Master’s or PhD in Computer Science, Electrical Engineering, Robotics, Computer Vision, or a related quantitative field.
  • 10+ years as a scientist or ML engineer, with experience leading end-to-end AI systems in production.
  • Deep expertise in computer vision (e.g., object detection, tracking, segmentation) for real-world environments.
  • Experience with multimodal perception and sensor fusion (e.g., camera, lidar, radar, GPS/IMU).
  • Experience with transformer-based architectures and vision-language models (VLMs/VLAs).
  • Experience building and deploying real-time or edge ML systems optimized for low-latency inference.
  • Demonstrated ability to drive systems from research through production deployment, including performance optimization and reliability at scale.

Nice to have

  • Experience applying perception to real-world systems such as driver assistance, fleet safety, or operations automation.
  • Publications or patents at top-tier venues (CVPR, ICCV, ICRA, NeurIPS).

What the JD emphasized

  • production
  • real-time
  • edge
  • constrained hardware
  • safety-critical systems
  • real-world environments
  • low-latency inference
  • performance optimization and reliability at scale

Other signals

  • runs in the real world, on real roads, protecting real drivers
  • models you build run on the edge, in real time
  • optimize and deploy models on constrained hardware
  • co-design the full stack
  • petabyte-scale multimodal data
  • Stay at the frontier of CV and perception research and translate what matters into shipped product
  • architect end-to-end computer vision pipelines for real-world safety detection
  • Drive the technical roadmap for edge and cloud perception
  • Mentor and technically guide senior scientists and engineers