About DoorDash USA
Connecting food lovers with local restaurants
Key Highlights
- Public company (NYSE: DASH) with a major IPO in 2020
- Over 450,000 restaurant partners across the U.S. and Canada
- $2.5 billion raised in funding from top investors
- Headquartered in San Francisco, CA with 1001+ employees
DoorDash is a leading local food delivery platform headquartered in San Francisco, CA, connecting food lovers with over 450,000 restaurants across the U.S. and Canada. Since its IPO in 2020, which was one of the largest of the year, DoorDash has raised $2.5 billion in funding and now employs over 1,...
🎁 Benefits
DoorDash offers unlimited paid time off, flexible work-from-home opportunities, comprehensive health insurance, a work-from-home stipend, and company ...
🌟 Culture
DoorDash fosters a competitive culture focused on innovation and transparency, particularly in its fee structures for restaurants. The company emphasi...
Machine Learning Engineer • Mid-Level
Skills & Technologies
Overview
DoorDash is hiring a Machine Learning Engineer to build and maintain a robust ML infrastructure that supports various company-wide workflows. You'll work with technologies like Python, AWS, and TensorFlow to enhance the efficiency of machine learning processes. This role requires a strong background in ML systems and infrastructure.
Job Description
Who you are
You have a strong background in machine learning and infrastructure, with a B.S., M.S., or PhD. in a relevant field. You are experienced in building scalable ML systems and have a deep understanding of data pipelines and model deployment. You thrive in collaborative environments, working closely with data scientists and product engineers to evolve ML platforms based on their needs. You are proficient in Python and familiar with cloud services like AWS, enabling you to design and implement robust solutions. You have experience with containerization technologies such as Docker and orchestration tools like Kubernetes, which are essential for managing ML workflows. You understand the importance of observability and reliability in ML systems, and you are committed to improving these aspects in your work.
Desirable
Experience with Apache Spark and MLflow is a plus, as these tools can enhance your ability to manage large-scale data processing and model tracking. Familiarity with PostgreSQL will also be beneficial for managing data storage and retrieval efficiently. You are eager to learn new technologies and adapt to evolving industry standards, ensuring that your skills remain relevant in a fast-paced environment.
What you'll do
In this role, you will be responsible for building a world-class ML platform that supports the development, training, and deployment of machine learning models. You will collaborate with data scientists to understand their use cases and translate them into scalable solutions. Your work will involve designing high-performance pipelines that can adapt to new technologies and modeling approaches, ensuring that the infrastructure can handle trillions of feature values and support hundreds of billions of predictions daily. You will also focus on improving the reliability and scalability of the training and inference infrastructure, making sure that it meets the demands of a 24x7 operational environment. You will have the opportunity to drive the direction of the centralized machine learning platform that powers all of DoorDash's business, making a significant impact on the company's operations.
What we offer
At DoorDash, you will be part of a dynamic team that is dedicated to building innovative solutions for the logistics industry. We offer a hybrid work environment, allowing you to balance remote work with in-office collaboration in San Francisco, Sunnyvale, or Seattle. You will have access to a wealth of delivery data, enabling you to make data-driven decisions that enhance the efficiency of our ML workflows. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds. Join us in our mission to create the world's most reliable on-demand logistics engine for delivery.
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