ML Platform Engineer — Build Scalable ML Pipelines

Doordashusa; San Francisco, Sunnyvale, Seattle (CA, CA, WA); Hybrid; USD 131,000 - 192,000; Full time

Benefits offered by this job

401(k) plan with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Job summary

DoorDash is seeking data scientists and ML engineers to leverage delivery data, improving ML workflows for search and recommendations, dasher assignment, ETA prediction, and capacity planning. This role is hybrid in San Francisco, Sunnyvale, or Seattle, with a small team focused on scalable ML platforms.

You will help design and deploy models, build high-performance pipelines, and ensure reliable training and inference infrastructure at scale, collaborating with data scientists and product

Qualifications

• BS/MS/PhD in Computer Science or equivalent is required.
• Strong knowledge of CS fundamentals and OOP concepts.
• 2+ years of industry experience in software engineering.
• Experience building ML systems in production and deploying models.
• Experience with data analytics at scale and ML tooling.

Responsibilities

• Dive into delivery data to improve ML workflows (Search & Recommendations, Dasher Assignment, ETA Prediction, capacity planning).
• Build scalable ML platforms and training/inference pipelines.
• Collaborate with Data Scientists and Product Engineers to meet use cases.
• Improve reliability, scalability, and observability of ML infra.

Skills

CS fundamentals

OOP languages

Data analysis

Cloud infrastructure

Education

BS/MS/PhD in CS or equivalent

Tools

Pandas

Python ML libraries

PyTorch

TensorFlow

Spark

MLFlow

Airflow

Dagster

Databricks

Job description

DoorDash is seeking data scientists and ML engineers to leverage delivery data, improving ML workflows for search and recommendations, dasher assignment, ETA prediction, and capacity planning. This role is hybrid in San Francisco, Sunnyvale, or Seattle, with a small team focused on scalable ML platforms.

You will help design and deploy models, build high-performance pipelines, and ensure reliable training and inference infrastructure at scale, collaborating with data scientists and product