About this role
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
• You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches
• You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day
• You will 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
• 2+ 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
Compensation
The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, a