Group Product Manager, ML Infrastructure

As a Group Product Manager at Waymo, you will oversee the multi-year product vision and roadmap for our internal ML Infrastructure platform. Your main responsibilities will include defining clear requirements, collaborating with cross-functional teams, and driving the end-to-end product lifecycle. You'll also prioritize initiatives that aim to enhance ML developer velocity and ensure that our platform effectively meets the needs of engineers and researchers.

To become a Group Product Manager at Waymo, you'll need a Bachelor's degree in Computer Science or a related field, along with extensive experience in product management, technical program management, or machine learning engineering. With 10+ years of combined experience and at least 5 years directly managing technical platforms or large-scale machine learning systems, you'll be well-prepared for the challenges ahead.

The Group Product Manager at Waymo plays a pivotal role in enhancing the infrastructure that supports our machine learning capabilities. By understanding the workflows of internal ML practitioners and implementing strategic improvements, you’ll be instrumental in refining the technology behind our autonomous driving solutions, enabling safer and more efficient rides for our users.

Essential skills for a Group Product Manager at Waymo include a deep understanding of the machine learning lifecycle, familiarity with ML frameworks like TensorFlow and PyTorch, and experience with cloud infrastructure. Moreover, proficiency in coordinating cross-functional teams and leading complex projects is crucial to effectively bridge the gap between technology and business needs.

As a Group Product Manager at Waymo, you will handle projects focused on improving our internal ML Infrastructure. This includes developing new capabilities for data management, large-scale training, and efficient deployment, as well as identifying challenges for MLOps and model optimization. Your work will help shape the future of our autonomous vehicle technology.

In answering this question, provide specific examples of projects you've led that involved managing large-scale machine learning systems. Highlight how you navigated challenges and collaborated with technical teams to ensure project success, showcasing your leadership and problem-solving skills.

Discuss your preferred communication methods and tools used to keep teams aligned. Mention specific strategies such as regular check-ins, collaborative planning sessions, and prioritizing feedback loops to ensure that all stakeholders are on the same page and that product priorities are effectively communicated.

Share your approach to gathering feedback from ML practitioners, emphasizing the importance of continuous engagement and collaborative discussions. Explain how you translate their requirements into actionable improvements, demonstrating an understanding of both user needs and technical feasibility.

Highlight your familiarity with ML frameworks such as TensorFlow, PyTorch, or JAX. Describe how this knowledge helps you make informed decisions about product features and capabilities, as well as how it enhances your ability to communicate effectively with engineering teams.

Provide a detailed account of a past project, focusing on your role, the challenges faced, and the strategies used to overcome those challenges. Emphasize collaboration, stakeholder management, and how you ensured successful delivery and adoption of the project.

Explain your methodology for prioritizing features, such as balancing user needs, technical feasibility, and strategic goals. Include how you gather input from stakeholders and assess the potential impact of various features on overall performance and user experience.

Discuss the significance of data management in supporting machine learning workflows. Demonstrate your understanding of the relationship between data quality and model performance, and explain how you ensure that robust data management practices are integrated into product development.

Talk about your conflict resolution strategies, which may include facilitating open discussions, finding common ground, and ensuring that all perspectives are heard. Highlight the importance of a collaborative environment and how you work to achieve consensus effectively.

Describe your practices for establishing key performance indicators (KPIs) for ML models and how you use monitoring tools to track these indicators. Detail how you leverage performance data to inform continuous improvements and address any issues promptly.

Express your passion for autonomous driving technology and how it aligns with your career goals. Talk about the thrill of working in a mission-driven environment and your desire to influence the future of transportation through innovative product management.