Machine Learning Engineer - Collision Avoidance System

The Collision Avoidance System (CAS) is responsible for detecting and reacting to imminent collision situations in support of our vehicle's overall safety goals. CAS Perception is responsible for processing raw sensor data from our vehicle's world-class sensor suite using a combination of geometric, interpretable algorithms and deep learning to detect near-collisions with obstacles along our intended driving path, in the most challenging dense urban environments and under tight compute resource constraints. Overall CAS is parallel and complementary to our Main Artificial Intelligence (AI) autonomy stack, and has a close relationship with our vehicle hardware and safety teams in order to architect redundancy into our overall driving system.

In this role, you will:

• Develop new algorithms that consume raw sensor data input to detect dynamic entities in the world
• Leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
• Engineer software that runs on-vehicle to efficiently execute our algorithms in real time
• Develop metrics and tools to analyze errors and understand improvements of our systems
• Collaborate with engineers on the other parts of CAS Perception, CAS Verification & Validation, CAS Planner, and the Main AI teams to solve the overall Autonomous Driving problem in complex urban environments

Qualifications

• BS, MS, or PhD degree in computer science or related field
• Experience with training and deploying Deep Learning models
• Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
• Fluency in C++
• Extensive experience with programming and algorithm design
• Strong mathematics skills

Bonus Qualifications

• Conference or Journal publications in Machine Learning or Robotics related venues
• Prior experience with Prediction and/or autonomous vehicles in general
• Fluency in Python