• Explore and analyze data using Python, pandas, and PySpark (or similar tools). • Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior patterns, content/topic clusters, expertise dimensions). • Create, tune, and productionize ML models for: • Categorization / classification • Recommendations and similarity • Other prediction or ranking tasks that power product features • Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use. • Build workflows where AI can: • Auto-fill or suggest key fields and metadata. • Proactively ask users/customers for missing or ambiguous information (e.g., via email or messaging). • Surface similar past items or solutions to assist humans in decision-making. • Fully handle simple, repetitive “Level 1” style requests end-to-end when safe to do so. • Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails. • Work with multiple product teams to help them identify and scope AI opportunities in their areas. • Define patterns, templates, and best practices for data ingestion, feature creation, model usage, and evaluation that teams can reuse. • Serve as a trusted advisor and technical lead: • Provide design and architecture guidance on data and ML-heavy features. • Join projects to handle the most complex modeling or workflow automation pieces when teams get stuck. • Mentor and guide junior data/ML engineers and analysts: • Conduct code and model reviews. • Pair with them on tricky problems. • Help them develop good intuitions about metrics, evaluation, and operational reliability. • Help establish and socialize standards for experimentation, documentation, and responsible AI usage across teams.

• Ensure systems align with real-world production workflows, tools, and operational constraints. • Define domain-specific acceptance criteria and quality standards for outputs. • Identify workflow mismatches early and guide teams toward solutions that fit existing production environments. • Validate prototypes and systems within realistic production scenarios, including edge cases and constraints. • Provide actionable feedback on usability, quality, and workflow integration. • Ensure outputs align with established domain standards, including formats, conventions, and downstream requirements. • Contribute directly to the development of new applied ML capabilities through validation scripts, data preparation workflows, evaluation datasets, prototype utilities, model behavior analysis, and integration tooling. • Partner with Applied ML Engineers to translate complex domain requirements into testable specifications, model behavior expectations, prototype workflows, and production-ready system behaviors. • Support evaluation design through representative examples, edge cases, and ground-truth data. • Work with the ML Behavior Systems Team to ensure systems meet defined evaluation and quality standards. • Validate that outputs meet professional expectations within the domain, beyond technical correctness. • Help interpret evaluation results in the context of real production use. • Ensure systems can be adopted within real production pipelines with minimal friction. • Validate compatibility with downstream tools, workflows, and operational environments. • Partner with Platform Integration teams to ensure outputs meet functional deployment requirements.

Role Description The Driver Understanding and Evaluation (DUE) team at Waymo is developing rich metrics for understanding the behavior of the Waymo Driver in the real world, and technologies such as context and scene analysis to understand driving, understanding and augmenting real world driving data to generate rare driving events, build large scale data infrastructure, improve components such as agents and a realistic simulator. These technologies come together to drive the overall technical strategy and methodology used to evaluate the behavior of the Waymo Driver. The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack. You will: - Lead the development of cutting edge Deep Learning and machine learning models to enhance human-led triaging and introduce automation for high-volume workflows. - Design and build Gen AI LLM/VLM solutions for self driving car behavior analysis and anomaly detection. - Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a Reinforcement Learning from human preference-based data collection and evaluation system. - Enhance User Feedback Analysis, collaborate seamlessly with product and business teams to design and implement tools for multi-label classifications, sentiment assessment, comment summarization, root cause analysis and trend analysis of rider feedback. - Oversee the production and optimization of machine learning models aiming to assess Waymo's expansive fleet of vehicles that cumulatively travel millions of miles. - Drive technical direction, and provide technical inputs and guidance to the team. - Work closely with PMs and TPMs to help define product requirements and align the technical agenda with the company's business objectives. - Collaborate closely with multiple teams (e.g., Prediction, Planning, Research), other technical leads, and senior leaderships across Waymo to deliver on key strategic efforts. Qualifications - B.S. in Computer Science, Robotics, Machine Learning, similar technical field of study, or equivalent practical experience. - 7+ years of experience with hands-on experience in machine learning projects. - Strong coding experience in C++ and/or Python. - Experience in at least one of: Foundational Models, VLM, Deep Learning. - Experience with ML frameworks such as TensorFlow, PyTorch, Hugging Face's transformers, along with expertise in deep learning models and ML deployment at scale. Requirements - M.S. or Ph.D. degree Computer Science or related quantitative field with a specialization of machine learning (preferred). - 10+ years of experience with hands-on experience in machine learning projects (preferred). - Deep learning experience with Transformers (preferred). - Gen AI LLM/VLM experience (preferred). - Experience with building tools for applied machine learning, including MLOps, evaluation/validation techniques, and model performance optimization (preferred). - Large-scale data processing and analytical skills (preferred). Benefits - Waymo employees are eligible to participate in Waymo’s discretionary annual bonus program. - Equity incentive plan. - Generous Company benefits program, subject to eligibility requirements. Salary Range The expected base salary range for this full-time position across US locations is $238,000 — $302,000 USD. 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.

• Design and develop ML infrastructure, tooling, and models to help teams deliver world class experiences • Build internal and external products and platforms to enable teams to incorporate AI into their features and customer facing products • Translate product goals into actionable engineering plans and build scalable, resilient services for data integration and event processing • Help product and development teams understand the data lifecycle and consult with teams on common ML patterns/tradeoffs • Coach and collaborate inside and outside the team to elevate technical standards • Write high-quality, performant, sustainable, and testable code while working in a cloud environment • Monitor the industry landscape, anticipate where technological advances are heading, and ensure Weave stays ahead of the curve • Cut through noise and hype to identify genuine strategic value; advocate for and lead key initiatives that prepare Weave for emerging challenges • Shape company-wide standards for engineering excellence, observability, and reliability in distributed systems • Actively mentor Staff and Senior Engineers across the fellowship, developing the next generation of technical leaders • Elevate architectural thinking across teams through design reviews, documentation standards, and hands-on guidance • Build organizational capability that persists beyond your individual contributions