Job Description:

• Architect end-to-end computer vision pipelines for real-world safety detection — object detection, tracking, semantic segmentation, multi-camera fusion, and beyond
• Drive the technical roadmap for edge and cloud perception, including how we optimize and deploy models on constrained hardware without sacrificing accuracy
• 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
• Work with petabyte-scale multimodal data (video, sensor, telematics, diagnostics) to train and iterate on production models
• Stay at the frontier of CV and perception research and translate what matters into shipped product
• Mentor and technically guide senior scientists and engineers across the team
• Bring clarity to ambiguous problems — translating customer and business needs into precise, solvable engineering challenges.

Requirements:

• 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.

Benefits:

• Flexible, employee-led remote model
• Professional development stipend
• Comprehensive health and parental leave plans
• Initial RSU grant with no vesting cliff
• Ongoing refresh opportunities tied to performance