ML Platform Engineer: Scalable Production ML Systems

DoorDash, Inc.; Seattle (WA); Hybrid; USD 131,000 - 192,000; Full time

Benefits offered by this job

401(k) with employer matching

16 weeks of paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Paid sick leave

Medical, dental, and vision benefits

11 paid holidays

Disability insurance

Life insurance

Family-forming assistance

Mental health program

Job summary

DoorDash, Inc. is building a production-grade ML platform. You will collaborate with data scientists and product engineers to deploy models and scale pipelines that process trillions of features and billions of predictions daily.

The role is hybrid in San Francisco, Sunnyvale, or Seattle, focusing on reliability, scalability, and cutting-edge ML infrastructure to power DoorDash's marketplace.

Qualifications

• Bachelor's/Master's/PhD in CS or equivalent.
• Strong CS fundamentals and OOP skills.
• 2+ years in software engineering.
• Experience building ML systems in production.
• Hands-on ML deployment and modeling experience.
• Cloud infrastructure experience; data processing systems are a plus.

Responsibilities

• Build and scale a world-class ML platform for model development, training, and deployment.
• Collaborate with data scientists and product engineers to evolve the ML platform.
• Design high-performance, scalable pipelines handling large feature stores and predictions.
• Contribute to centralized ML directions powering DoorDash's business.
• Improve reliability and observability of training and inference infra.

Skills

CS fundamentals

OOP languages

Software engineering

ML systems

Education

B.S., M.S., or PhD in Computer Science

Tools

Python

Pandas

PyTorch

TensorFlow

Spark

MLFlow

Airflow

Dagster

AWS

Job description

DoorDash, Inc. is building a production-grade ML platform. You will collaborate with data scientists and product engineers to deploy models and scale pipelines that process trillions of features and billions of predictions daily.

The role is hybrid in San Francisco, Sunnyvale, or Seattle, focusing on reliability, scalability, and cutting-edge ML infrastructure to power DoorDash's marketplace.