Machine Learning Engineer, Model Optimization

The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the self-driving car, i.e., the system that "perceives" the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware.

In this hybrid role you will report to a Technical Lead Manager.

You will:

• Optimize FLOPs utilization in model training and model inference through model architecture/ hardware co-development, optimize for a naturally sparse representation (most spatial-temporal information in self-driving is sparse).
• Optimize model inference for different onboard and offboard (simulation) platforms.
• Analyze and optimize real-time inference of complex model architectures with many model components as well as on the critical path within an onboard system.

You have:

• Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience
• 3+ years experience in Machine Learning and/or Computer Vision
• Experience with Python
• Experience with ML frameworks like PyTorch or JAX

We prefer:

• MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline
• Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI
• Experience with C++