About the Team
Come help us build the world's most reliable on-demand logistics engine for delivery! We are bringing on a talented Machine Learning Engineer to help us improve the delivery service quality for DoorDash's three-sided marketplace of consumers, merchants, and dashers. As a fundamental area of investment for DoorDash, Delivery Excellence has among the coolest problems to solve at scale and creates a major impact on the company and our customers.
About the Role
As a Machine Learning Engineer, you will leverage our robust data and machine learning infrastructure to develop inference and ML models that impact millions of users across our three audiences and tackle our most challenging business problems. You will work with other engineers, analysts, and product managers to develop and iterate on models to help us grow our business and provide the best service quality for our customers.
You're excited about this opportunity because you will…
- Build statistical and ML models that run in production to help enhance the consumer experience by reducing cancellations, pickup waiting times, delivery lateness, missing and incorrect items, and non-fulfilled orders.
- Own the modeling life cycle end-to-end including feature creation, model development and prototyping, experimentation, monitoring and explainability, and model maintenance.
- Be exposed to new opportunities where delivery quality can be used as a lever for demand shaping, search ranking, customer segmentation, etc.
- Mentor and uplevel a talented team of ML Engineers.
We're excited about you because…
- High-energy and confident — you keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down.
- You're an owner — driven, focused, and quick to take ownership of your work.
- Humble — you're willing to jump in and you're open to feedback.
- Adaptable, resilient, and able to thrive in ambiguity — things change quickly in our fast-paced startup and you'll need to be able to keep up!
- Growth-minded — you're eager to expand your skill set and excited to carve out your career path in a hyper-growth setting.
- Desire for impact — ready to take on a lot of responsibility and work collaboratively with your team.
Experience
- 1+ years of industry experience post-PhD or 3+ years of industry experience post-graduate degree developing machine learning models with business impact.
- M.S. or PhD in Machine Learning, Statistics, Computer Science, Applied Mathematics, or other related quantitative fields.
- Demonstrated expertise with programming languages, e.g., Python, SciKit Learn, LightGBM, Spark MLLib, PyTorch, TensorFlow, etc.
- Deep understanding of complex systems such as marketplaces, and domain knowledge in two or more of the following: Machine Learning, Causal Inference, Operations Research, Forecasting, and Experimentation.
- Experience of shipping production-grade ML models and optimization systems, and designing sophisticated experimentation techniques.
- You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.
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