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Waymo's Technical 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 you will report to a Technical Program Manager.

You will:

• Drive cross-functional execution and delivery of projects within the onboard team, collaborating with engineering teams, product teams and release management
• Manage project schedules, identify risks and dependencies, and develop mitigation strategies
• Facilitate communication and collaboration between engineering, research, and product teams, ensuring alignment on program goals, milestones, and technical requirements
• Track and report on project/program progress, risks, and key performance indicators (KPIs) to leadership and stakeholders
• Contribute to the strategic planning, objective setting, and technical roadmap for the Waymo Foundation Model program
• Proactively identify and resolve roadblocks and dependencies, coordinating across organizational boundaries

You have:

• A Bachelor's degree in Computer Science, Engineering, or a related technical field
• 5+ years of experience as a Technical Program Manager in a software engineering environment
• A proven track record of successfully managing complex technical projects involving machine learning and/or AI
• Experience with machine learning models, training pipelines, or evaluation frameworks
• Excellent communication, interpersonal, and presentation skills, effectively conveying technical concepts to both technical and non-technical audiences
• Strong analytical, problem-solving, and technical judgment skills

We prefer:

• Experience with large language models (LLMs), vision language models, generative AI (diffusion models), large-scale model training, optimization, and scaling techniques
• Experience with cloud-based machine learning platforms and infrastructure
• Strong background in 3D computer vision, sensor fusion (camera, lidar), spatial reasoning, and 3D reconstruction methods (NeRF, Gaussian Splatting)
• Experience with simulation environments for autonomous systems, model validation strategies, and methods to ensure model reliability
• Experience with model evaluation techniques, metrics, time-series analysis, and predictive modeling using sensor data
• Master's degree or PhD in a related field