Machine Learning Engineer - Experimentation Platform

DoorDash is seeking a Principal Machine Learning Engineer to join their Experimentation Platform team, which develops an industry-leading platform for data scientists and ML engineers. The role focuses on building and evolving sophisticated experimentation systems that combine statistical methodologies, machine learning, and artificial intelligence.

The position requires deep expertise in both theoretical and practical aspects of experimentation, causal inference, and machine learning. You'll be working on cutting-edge problems in A/B testing, adaptive learning, and causal inference platforms that support DoorDash's three-sided marketplace. The team runs thousands of experiments monthly and is dedicated to democratizing experimentation with quality and velocity.

As a Principal Engineer, you'll collaborate with a diverse team of backend, web, statistical, and data infrastructure engineers, working closely with the data science community. You'll be responsible for expanding statistical algorithms, implementing ML solutions, and advising teams across the company on experimental design.

The role offers competitive compensation ($231,200 - $340,000), comprehensive benefits, and the opportunity to work on challenging problems at scale. You'll be part of a company that values diversity, inclusion, and empowering local economies through technology. The position is based in either San Francisco Bay Area or Seattle, where you'll help shape the future of DoorDash's data-driven decision-making processes.

This is an ideal role for someone with both strong technical skills and the ability to influence product direction through data-driven insights. You'll have the chance to publish your work and contribute to the broader technical community, as evidenced by DoorDash's various technical publications on experimentation.