Software Engineer, Machine Learning Platform - MDX

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.

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

• 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

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, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.

In addition to base salary, the compensation