What you’ll be doing:

• Apply first principles, applied theory and machine learning to solve challenging perception problems for autonomous systems (e.g. object detection, semantic segmentation, instance segmentation, dense depth, optical flow, tracking, etc.)
• Refine and optimize models for low-latency on embedded hardware
• Characterize and quantify the performance of the vision systems 
• Research, prototype, and experiment with various sensors, algorithms and approaches
• Architect, design, and implement software applications, infrastructure, and tools
• Write performant, well-tested software, improve code quality through code and design reviews

What you’ll bring:

• Strong proficiency in C++11 (or newer) development for resource constrained embedded systems
• 5+ years of experience in software development for problems involving state of the art computer vision, machine learning and robotic perception techniques
• Experience handling large datasets efficiently
• Experience curating synthetic and real-world image datasets
• Experience building algorithms for various hardware sensors (eg, LiDAR, camera, radar, etc.)
• Strong mathematical skills and understanding of probabilistic techniques
• Ability to thrive in a fast-moving, collaborative, small team environment with little supervision
• Excellent analytical, communication, and documentation skills with demonstrated ability to collaborate with interdisciplinary stakeholders
• Passion for sustainable energy and electric vehicle development

What makes you a strong fit:

• Experience with real-time sensor fusion, and 3D data representations (pointclouds, meshes, etc.)
• Experience with parallel processing using hardware building blocks (CUDA, DLAs, etc.)
• Experience building and expanding ML model architectures
• Experience with compute constrained pipelines and architectures, optimizing architectures to explore the accuracy vs. compute tradeoff
• Master’s or PhD in Robotics, Computer Science, Computer Engineering, Electrical Engineering, related field or equivalent industry experience