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
• 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
• 4+ 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 location-specific base salary range for this position is listed below. Compensation in other geographies may vary.

Actual compensation within the pay range will be decided based on factors including, but not limited to, skills, prior relevant experience, and specific work location. For roles that are available to be filled remotely, base salary is localized according to employee work location. Please discuss your intended work location with your recruiter for more information.

DoorDash cares about you and your overall well-being, and that’s why we offer a comprehensive benefits package, for full-time employees, that includes healthcare benefits, a 401(k) plan including an employer match, short-term and long-term disability coverage, basic life insurance, wellbeing benefits, paid time off, paid parental leave, and several paid holidays, among others.

In addition to base salary, the compensation package for this role also includes opportunities for equity grants.

We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on June 20, 2024.

Please see the independent bias audit report covering our use of Covey here .

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