What are the responsibilities and job description for the Machine Learning Engineer, Experimentation position at DoorDash USA?
About the Team
The Experimentation Platform Team develops the state-of-art platform in industry that enables Data Scientists, ML Engineers and non-technical audience to come up with hypotheses; design, set up and analyze the experiments; and conduct exploratory and causal analysis. At DoorDash, 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 and work closely with the data science community.
Ready to make your application Please do read through the description at least once before clicking on Apply.
About the Role
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, DoorDash’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.
You're excited about this opportunity because you will...
- 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.
We're excited about you because you have…
Deep expertise in mathematics, statistics, causal inference or econometrics.
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