Sr. Machine Learning Engineer, Marketplace ML Platform

About the role

This position centers on the economic systems that power Waymo's ride-hailing services. The role owner defines the rules that connect riders with vehicles and maintain the commercial health of the fleet. Responsibilities include designing logic for dynamic pricing and high-value matches while ensuring decisions remain safe and efficient at scale. The role requires building data pipelines that enable rapid pickups and protect profitability. Success in this position means driving both operational reliability and financial performance for the marketplace. You will be responsible for architecting the core algorithms that balance supply and demand across a dynamic urban network. This role demands rigorous analysis of marketplace health metrics to guide strategic product and engineering decisions. You will translate complex business requirements into scalable machine learning solutions that directly impact revenue and customer satisfaction. The position requires a deep understanding of how algorithmic controls influence real-world fleet behavior and user experience.

Key facts

This full-time position is based in Mountain View, California, United States. The expected base salary range is $213,000 to $263,000 USD. Actual compensation will vary based on work location, experience, training, education, and skill level. Candidates may receive details about specific local ranges during the hiring process. Waymo employees are eligible for a discretionary annual bonus program, an equity incentive plan, and a comprehensive benefits package, subject to eligibility requirements.

What you'll do

You will implement and scale the core economic engine for Waymo's ride-hailing commercial services. This includes designing the algorithms that match riders to vehicles, optimize routing, balance supply and demand, forecast rider demand, and enable dynamic pricing. You will develop high-level infrastructure code to automatically train and deploy sophisticated optimization and prediction models. These models support real-time decisions related to pricing, matching, and vehicle positioning. A key responsibility is establishing model life cycle workflows. You will handle feature engineering, training workflows, and inference services to ensure reliability across the system. You will also create next-generation simulation capabilities. These tools will allow the team to run high-fidelity virtual experiments to test marketplace behavior. Streamlining experimentation practices is another core duty, aimed at delivering a unified and user-friendly platform for marketplace testing. You will partner closely with optimization teams to align routing, supply, and demand strategies. Defining data pipelines that feed timely features into production models is essential. These models serve critical flows across the network. You will also guard system integrity by validating inputs, outputs, and behavior before releases reach users. This ensures the fleet operates safely and profitably across cities. You will lead cross-functional initiatives to integrate new research findings into production-grade marketplace features. This role requires constant evaluation of model performance against key business and safety indicators. You will mentor other engineers on best practices for deploying machine learning in high-stakes operational environments.

Requirements

You must hold a BS degree in Computer Science or demonstrate equivalent hands-on experience in complex systems. You should have 5 or more years of experience writing backend code in Java or C++ in production services. You must have a track record of shipping platforms that support multiple product use-cases across marketplace and optimization teams. You should apply Python for machine learning using mature frameworks such as TensorFlow, PyTorch, or Keras. Prior experience in building backend platforms that support multiple services is essential. Demonstrated experience working on AV or mobility technology stacks is highly relevant. You must be proficient in designing systems that handle large-scale data with strict reliability requirements. Experience with distributed computing concepts and cloud infrastructure is expected. You should have a strong grasp of software engineering fundamentals, including version control, testing, and code review. The ability to communicate technical trade-offs clearly to both engineering and business stakeholders is critical. You must be comfortable working in an environment where safety, reliability, and precision are non-negotiable priorities.

Nice to have

Holding an MS in Computer Science or comparable real-world exposure is preferred. Experience delivering ML and optimization models into demanding production environments is valuable. You should have experience constructing ML data pipelines and automation atop mature infrastructure. Contributing at ride-hailing or marketplace companies that shape commercial decisions is a strong advantage. Completion of coursework focused on ML and optimization methods is also a plus. Any experience with simulation tools or experimentation platforms will support success in this role. Experience with reinforcement learning for pricing or matching problems is considered a significant asset. Knowledge of geospatial data processing and routing algorithms is highly relevant to this position.

Practical notes

Details may vary slightly based location and individual qualifications. Ensure your application reflects your direct experience with marketplace systems, ML infrastructure, and backend development.