Senior Staff Software Engineer, Experimentation Platform

DoorDash is seeking a Senior Staff Software Engineer to join their Experimentation Platform team, which develops an industry-leading platform enabling data scientists, ML engineers, and non-technical users to design, run, and analyze experiments. This role sits at the intersection of statistics, machine learning, and engineering, working on cutting-edge experimentation algorithms alongside some of the industry's brightest minds.

The position requires deep expertise in both theoretical and practical aspects of statistical methodologies, machine learning, and artificial intelligence technologies. You'll be responsible for building and evolving the experimentation platform to handle advanced causal inference and data mining techniques, while supporting large-scale experimentation volume in a high noise-to-signal business environment.

The role offers an exciting opportunity to work with a mix of experienced veterans in backend, web, statistical, and data infrastructure engineering. You'll be directly contributing to DoorDash's data-driven decision-making process, implementing solutions that power their intelligent, last-mile delivery platform. The team's work has been featured in multiple published articles and has helped increase DoorDash's logistics experiment capacity by 1000%.

The compensation package is highly competitive, ranging from $231,200 to $340,000 USD base salary, plus equity grants. DoorDash offers comprehensive benefits including medical, dental, vision coverage, 401(k) matching, generous parental leave, and wellness benefits. The position requires 10+ years of industry experience and an advanced degree in a quantitative field.

This is an ideal opportunity for someone passionate about applying advanced statistical and machine learning concepts to real-world problems, working with big data technologies, and making a significant impact on a platform that runs thousands of experiments monthly. The role offers the chance to work on challenging problems while collaborating with cross-functional teams to drive innovation in experimentation and causal inference.