Senior Staff Machine Learning Engineer, LLM/VLM Model Architecture & Optimization

The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), 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.

- Design VLM/LLM model architecture and drive strong alignment between model architectures and hardware architectures.

- Optimize model performance for on-device use cases (memory, power, compute constrained environments).

- Engage directly with research, software engineering, hardware engineering, and product teams to deliver end-to-end solutions.

- 7+ years of experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models).

- Proven expertise in low-latency on-device inference techniques and a deep understanding of hardware acceleration.

- Extensive experience with deep learning frameworks (e.g. PyTorch, JAX) and large-scale model training.

- A track record of operating effectively under ambiguity, setting direction amid rapidly evolving research and technical constraints

- Experience applying large language models or foundation models in complex, safety-critical domains (e.g., autonomy, robotics, or other high-reliability systems)

- Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.

- Familiarity with large-scale data curation and quality assurance processes for multimodal datasets.

- Background in autonomous vehicle perception, motion planning, or decision-making systems.

- Publications in top-tier machine learning or computer vision conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV).