Technical Lead Manager, Developer AI

Waymo is on a mission to create the "World's Most Trusted Driver", and the Waymo ML Infrastructure team is accelerating that mission by building the best ecosystem for sustainably building and shipping ML-powered intelligence across the entire model development lifecycle. As we scale our autonomous driving technology, we are leaning into Developer AI (DevAI) to help enable our developers to rapidly solve the complex challenges of making real-time decisions in our engineering environments, optimizing performance on constrained hardware, and ensuring the efficiency and accuracy of our machine learning models. As TLM of the DevAI team, you will have a unique opportunity to contribute to a field at the forefront of AI innovation by developing best-in-class DevAI tools to solve mission-critical problems and accelerate developer productivity.

You will:

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  Report into our Head of Infra Foundations

  
  
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  Build and lead a team responsible for Waymo's DevAI efforts

  
  
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  Develop an agentic ecosystem to solve core developer journeys

  
  
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  Build and tune models for Waymo-specific code, data, and use-cases

  
  
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  Facilitate adoption of DevAI tools across Waymo with training and advocacy

  
  

You have:

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  Education: BS, MS, or PhD degree in CS, ML, AI, or a related quantitative field. Equivalent practical experience will also be considered.

  
  
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  Industry Experience: 8+ years of industry experience in software engineering, with at least 5 years focused on ML/AI or Developer Productivity projects.

  
  
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  Technical Leadership: 3+ years of experience leading technical teams, with an emphasis on promoting a collaborative and innovative environment.

  
  
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  Technical Skills: Excellent programming skills in Python and C++. Experience building scalable, and performant software systems.

  
  
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  Strategic Vision: Ability to define a strategic technical roadmap for DevAI, balancing short-term tactical goals with long-term progress.

  
  
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  Collaboration: Experience working with teams to gather requirements, increase adoption, and integrate AI solutions.

  
  
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  Continuous Learning: Experience staying current with the latest advancements in AI, ML, and DevProd, and a desire to apply the latest solutions to real-world problems.

  
  

We prefer:

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  Agentic Systems Development: Practical experience designing, developing, and deploying agentic AI systems and frameworks (e.g., using frameworks like AutoGen, LangChain, Semantic Kernel). Understanding of multi-agent systems and their application.

  
  
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  Model Building & Tuning: Demonstrated experience in building, fine-tuning, evaluating, and deploying large language models (LLMs) and other advanced ML models for specific domains and use cases. Experience with techniques like RLHF (Reinforcement Learning from Human Feedback) is a plus.

  
  
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  Data Expertise: Experience with data cleaning, ingestion, and processing for large-scale ML model training, for code and domain-specific datasets.

  
  
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  Developer Productivity: Experience in areas related to large-scale software development, developer tools, or internal platforms. Familiarity with code analysis, static analysis, or developer productivity tools would be a significant asset.

  
  
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  Advocacy & Training: Experience facilitating adoption of new tools and technologies through training, documentation, and advocacy within a large engineering organization.