Software Engineer, Operational/ Process Efficiency

Waymo•Mountain View, CA

3d•$175,000 - $215,000•Hybrid

About The Position

This role is at the intersection of robotics and machine learning, driving the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet. You will lead efforts to generalize complex depot operations—such as exterior cleaning, sensor calibration, and maintenance checks—using advanced robotics. Key work involves leveraging foundation models, reinforcement learning, simulation, and integrating ML models in production at scale. You will interface closely with operations teams to translate real-world needs into robust, working solutions.

Requirements

A Masters or PhD in Machine Learning, Computer Science, or a related technical field.
A proven track record of delivering working engineering solutions, balancing scientific rigor with production needs.
Experience in training, evaluating, and deploying machine learning models at scale.
Strong communication skills and the ability to collaborate across multidisciplinary teams (from field technicians to hardware designers).

Nice To Haves

Hands-on experience or deep familiarity with agentic tools and frameworks.
Experience working with large-scale foundation models (LLMs, VLMs) and fine-tuning them for specialized domains.
Background in automating industrial or hardware-centric workflows.
Familiarity with hardware diagnostics, failure analysis, or manufacturing processes.

Responsibilities

Drive the automation of the hardware lifecycle for critical sensors (lidar, radar, cameras) and compute modules.
Develop and deploy agentic systems and foundation models to streamline workflows between internal teams and contract manufacturers.
Identify opportunities to apply AI to manufacturing, installation, and troubleshooting processes to increase operational velocity.
Interface with a diverse set of stakeholders, including hardware design engineers, failure analysis engineers, and diagnostic teams, to translate physical requirements into technical specifications.
Bridge the gap between experimental ML models and high-scale production environments.