ML Infrastructure Engineer — Agentic Systems

DoorDash, Inc.; Sunnyvale (CA); On-site; 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 and paid sick leave

Medical, dental, and vision benefits

11 paid holidays

Disability and basic life insurance

Family-forming assistance

Mental health program

Job summary

DoorDash, Inc. is hiring for a role within the MDX team focused on building scalable ML infrastructure across the company’s ML platform. You will own end-to-end components, from design to production, enabling agents to accelerate ML workflows across multiple regions and partners.

The position emphasizes back-end engineering with a strong emphasis on Python and distributed systems, feature stores, data pipelines, and observability to ensure reliable ML deployments and experiments.

Qualifications

• Experience building ML infrastructure in production including feature engineering and feature stores.
• Data pipelines for ML, training and experimentation platforms, model evaluation and deployment.
• Strong backend engineering fundamentals, especially in Python and distributed systems.

Responsibilities

• Build agentic systems that accelerate ML development across platforms.
• Design infrastructure to run experiments and evaluate modeling ideas.
• Develop tooling to support the ML lifecycle, including training jobs and model deployments.
• Improve observability and reliability of ML workflows and pipelines.
• Collaborate with ML engineers, data scientists, and platform teams to scale workflows.

Skills

Python

Distributed systems

Backend engineering

ML infrastructure

Feature stores

Data pipelines

Model evaluation

Deployment

Observability

Education

B.S./M.S./PhD in CS or equivalent

Tools

Kubeflow

MLflow

Ray

Airflow

Kubernetes

AWS

GCP

Job description

DoorDash, Inc. is hiring for a role within the MDX team focused on building scalable ML infrastructure across the company’s ML platform. You will own end-to-end components, from design to production, enabling agents to accelerate ML workflows across multiple regions and partners.

The position emphasizes back-end engineering with a strong emphasis on Python and distributed systems, feature stores, data pipelines, and observability to ensure reliable ML deployments and experiments.