Job Description

About the Team

Come help us build the world's most reliable on-demand logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.

About the Role

At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search & Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale, or Seattle.

You’re excited about this opportunity because you will…

• Build a world-class ML platform where models are developed, trained, and deployed seamlessly.
• Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases.
• Help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques, and modeling approaches.
• Work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day.
• Help design and drive directions for the centralized machine learning platform that powers all of DoorDash's business.
• Improve the reliability, scalability, and observability of our training and inference infrastructure.

We’re excited about you because…

• B.S., M.S., or PhD. in Computer Science or equivalent.
• Exceptionally strong knowledge of CS fundamental concepts and OOP languages.
• 6+ years of industry experience in software engineering.
• Prior experience building machine learning systems in production such as enabling data analytics at scale.
• Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts.
• Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems.

Nice To Haves

• Experience with challenges in real-time computing.
• Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure.
• Familiar with Pandas and Python machine learning libraries and deep learning frameworks such as PyTorch and TensorFlow.
• Familiar with Spark, MLLib, Databricks, MLFlow, Apache Airflow, Dagster and similar related technologies.
• Familiar with large language models like GPT, LLAMA, BERT, or Transformer-based architectures.
• Familiar with a cloud-based environment such as AWS.

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