Job description

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. Waymo’s Technical Program Managers and Program Managers are accountable for Waymo’s roadmap execution by providing thoughtful cross-functional planning, clarity, and proactive risk management. In the face of complex technical and operational challenges with no established playbooks to follow, we act with thoughtful urgency, driving conversations, discussions, and outcomes. Our team partners closely with every function of Waymo to structure, own and drive work towards real-world deployments of the Waymo Driver across platforms and geographies. This role follows a hybrid work schedule and reports and you will report to a Director of Technical Program Management. You will: Program Execution: Own and drive the execution of large, cross functional programs within the Simulation and ML Evaluation domain, translating technical roadmaps into structured project plans ML Integration : Drive the execution of programs that integrate simulation deeply into the ML development loop, establishing a seamless, reliable path for model training, offline evaluation, and validation Dependency & Risk Management : Proactively identify, track, and mitigate technical dependencies, risks, and blockers across onboard software and offboard infrastructure teams Technical Judgment: Exercise strong technical judgment to contribute to tool/platform design discussions, evaluate architectural trade offs, and ensure deliverable quality Operations & Release Coordination : Coordinate releases, manage tool deployment, and oversee triage workflows for simulation platforms to ensure high reliability and usability for developer teams Process Scaling: Identify systemic bottlenecks in the offline evaluation loop and implement process or tooling improvements to scale simulation capabilities and efficiency You have: Education : Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience TPM Experience : 3+ years of experience as a Technical Program Manager in a software engineering or large scale infrastructure environment ML Experience: 2+ years of experience managing technical projects involving machine learning infrastructure, model training/evaluation loops, or ML data pipelines Program Ownership : Proven track record of owning and driving complex technical programs end-to-end, managing timelines, risks, and dependencies across multiple stakeholders Technical Judgment : Strong technical judgment with the ability to engage in design discussions with engineering leads and analyze technical trade offs Communication : Excellent communication and interpersonal skills, with the ability to convey complex technical concepts to both research and infrastructure teams We prefer: MLOps & Tooling: Experience with ML frameworks (JAX, PyTorch) and cloud based ML training/evaluation platforms ML Based Simulation : Familiarity with advanced simulation technologies, including ML based scene generation (e.g. NeRF, Gaussian Splatting, Diffusion, etc.) and reactive agent modeling Simulation: Familiarity with autonomous system simulation, log playback infrastructure, or closed-loop model validation strategies Data Management : Experience with large scale dataset management, data validation frameworks, or unified data solutions Developer Platforms : Experience building or managing developer tools or platforms that simplify complex developer workflows AV Domain Knowledge : Prior experience in the autonomous vehicle industry or robotics, understanding onboard/offboard dependencies Advanced Degree : Master's degree in a technical field, ideally related to AI/ML The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $196,000 — $242,000 USD

Requirements

Must-haves

This role follows a hybrid work schedule and reports and you will report to a Director of Technical Program Management.
Program Execution: Own and drive the execution of large, cross functional programs within the Simulation and ML Evaluation domain, translating technical roadmaps into structured project plans
ML Integration : Drive the execution of programs that integrate simulation deeply into the ML development loop, establishing a seamless, reliable path for model training, offline evaluation, and validation
Dependency & Risk Management : Proactively identify, track, and mitigate technical dependencies, risks, and blockers across onboard software and offboard infrastructure teams
Technical Judgment: Exercise strong technical judgment to contribute to tool/platform design discussions, evaluate architectural trade offs, and ensure deliverable quality
Operations & Release Coordination : Coordinate releases, manage tool deployment, and oversee triage workflows for simulation platforms to ensure high reliability and usability for developer teams
Process Scaling: Identify systemic bottlenecks in the offline evaluation loop and implement process or tooling improvements to scale simulation capabilities and efficiency
Education : Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
TPM Experience : 3+ years of experience as a Technical Program Manager in a software engineering or large scale infrastructure environment
ML Experience: 2+ years of experience managing technical projects involving machine learning infrastructure, model training/evaluation loops, or ML data pipelines
Program Ownership : Proven track record of owning and driving complex technical programs end-to-end, managing timelines, risks, and dependencies across multiple stakeholders
Technical Judgment : Strong technical judgment with the ability to engage in design discussions with engineering leads and analyze technical trade offs
Communication : Excellent communication and interpersonal skills, with the ability to convey complex technical concepts to both research and infrastructure teams
MLOps & Tooling: Experience with ML frameworks (JAX, PyTorch) and cloud based ML training/evaluation platforms
ML Based Simulation : Familiarity with advanced simulation technologies, including ML based scene generation (e.g. NeRF, Gaussian Splatting, Diffusion, etc.) and reactive agent modeling
Simulation: Familiarity with autonomous system simulation, log playback infrastructure, or closed-loop model validation strategies
Data Management : Experience with large scale dataset management, data validation frameworks, or unified data solutions
Developer Platforms : Experience building or managing developer tools or platforms that simplify complex developer workflows
AV Domain Knowledge : Prior experience in the autonomous vehicle industry or robotics, understanding onboard/offboard dependencies
Advanced Degree : Master's degree in a technical field, ideally related to AI/ML

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

mlmachine learningai

Platform: greenhouse
Effort: quick