Senior Software Engineer, Experimentation Platform

Senior Staff Software Engineer, Experimentation Platform

San Francisco, CA;
Seattle, WA;
Sunnyvale, CA

The Experimentation Platform team develops an industry-leading platform that enables data scientists, ML engineers and non-technical users to design, run and analyze experiments and conduct exploratory and causal analysis. At Door Dash, where we run thousands of experiments per month, our mission is to equip all decision makers with rigorous, data-driven insights by democratizing experimentation with quality and velocity. The team consists of a mix of experienced veterans of backend, web, statistical and data infra engineers that works closely with the data science community.

If you embrace the challenges in the intersection of statistics, machine learning, and engineering, build cutting-edge experimentation algorithms and work with some of the smartest people in the industry, Door Dash’s Experimentation Platform is the right place for you. Come join us and be part of the mission.

You will partner with backend and frontend engineers and build platforms to power data-driven decision making. Solutions will inform how we build our intelligent, last-mile delivery platform for local cities and will be on the forefront of research conducted on three-sided marketplaces.

• Build Experimentation platform that can evolve to handle new statistical methodologies, machine learning and artificial intelligence technologies and advanced causal inference and data mining techniques
• Drive the statistical and ML development of internal platforms, including both the theoretical and engineering aspects, products including A/B testing platform, Causal Inference platform and Adaptive Learning platform (RL/MAB)
• Expand the statistical and causal inference algorithms to support large-scale experimentation volume and computation load and high noise-to-signal business environment
• Apply semi-supervised learning, LLM, active learning, documentation embedding/retrieval and data augmentation strategies to advance the hypothesis generation of the experimentation platform
• Advise data scientists, operators, and engineers across the company on experimental design and adoption of experimentation tools

• 10+ years of industry experience of developing statistical or ML models with business impacts
• M.S., or PhD. in Statistics, Causal Inference, Experimentation, Computer Science, Applied Mathematics or other related quantitative fields
• Demonstrated expertise with programming languages, e.g. Python, Java, Kotlin, Go, Sci Kit Learn, Spark MLLib, etc.
• Experience building reliable, scalable, highly available distributed systems
• You are located or are planning to relocate to San Francisco CA, Sunnyvale, CA, or Seattle, WA
• Nice to haves
• Deep expertise in mathematics, statistics, causal inference or econometrics
• Fullstack industry experience (web + backend)
• Experience with any of the “Big Data” technologies (e.g. Postgres, Redis, Elasticsearch, Snowflake, Mode, Segment, Spark etc.)

At Door Dash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started with door-to-door delivery, and we are looking for team members who can help us go from a company that is known for delivering food to a company that people turn to for any and all goods.

Door Dash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We re committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and…