Software Engineer, Machine Learning Platform - MDX
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, 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 for this role includes opportunities for equity grants. Talk to your recruiter for more information.
DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.
To learn more about our benefits, visit our careers page here.
See below for paid time off details:
For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.
For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).
The national base pay range for this position within the United States, including Illinois and Colorado.
$130,600—$192,000 USD