Senior Staff Software Engineer, Model Post Training

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

We are seeking highly motivated and talented experienced engineers to join our team to lead the post-training LLM for autonomous driving. You will be working alongside a world-class team of researchers and engineers to develop and advance the next generation of frontier AI models.

You will: 

• Post-training for instruction following and CoT thinking: Develop and apply methods to enhance the instruction following and CoT abilities of LLM models targeting autonomous driving applications. This may include fine-tuning strategies, reinforcement learning techniques, or novel approaches to improve LLM’s ability to solve complex driving tasks, make decisions, and interact with dynamic environments.


• Collaborate deeply with research teams asking the right technical questions, translating research advances into production-ready systems, and shaping joint technical direction.


• Provide technical leadership to influence senior engineers and researchers across ML, infra, and data teams.


• Raise the technical bar for how Waymo trains, evaluates, and deploys LLM models in the autonomous driving technical stack.
  
  

You have:

• Demonstrated experience developing post-training methods for LLM models


• 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


• The ability to translate research insights into production-ready post-training pipelines and systems


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


• Excellent communication and collaboration skills