ML Infrastructure Engineer — Agentic Systems

DoorDash USA; San Francisco, Sunnyvale, Seattle (CA, CA, WA); Vor Ort; USD 131.000 - 192.000; Vollzeit

Benefits dieser Stelle

401(k) matching

Paid parental leave

Medical, dental, vision benefits

Paid time off & holidays

Zusammenfassung

DoorDash's MDX team seeks a Software Engineer for ML Infrastructure (MDX) to join our San Francisco, Sunnyvale, or Seattle offices. You will design and build the agentic layer of the ML platform for faster experimentation, training, deployment, and observability across teams.

You will own end‑to‑end components, work with backend systems in Python, and collaborate closely with ML engineers, data scientists, and platform teams to advance model development and reliability.

Qualifikationen

• BS/MS/PhD in CS or equivalent.
• 2+ years of industry software engineering experience.
• Hands‑on experience building ML infrastructure in production (feature stores, data pipelines, training/experimentation, evaluation, deployment, serving, inference or monitoring).
• Strong Python and distributed systems fundamentals.
• Experience with production OS practices: testing, observability, debugging, reliability.
• Ability to translate ambiguous problems into production solutions and learn quickly.

Aufgaben

• Build agentic systems to accelerate ML development across teams.
• Design infrastructure for proposing, running, and evaluating modeling experiments.
• Develop agentic developer tools for training, debugging, deployments, and platform navigation.
• Develop evaluation and observability for agent behavior to catch regressions and build trust.

Kenntnisse

Python

Distributed systems

ML infrastructure

Backend engineering

Observability

Production systems

AI coding tools

Ausbildung

B.S./M.S./PhD in Computer Science or equivalent

Tools

Kubernetes

AWS/GCP

Kubeflow / MLflow / Ray / Airflow

Jobbeschreibung

DoorDash's MDX team seeks a Software Engineer for ML Infrastructure (MDX) to join our San Francisco, Sunnyvale, or Seattle offices. You will design and build the agentic layer of the ML platform for faster experimentation, training, deployment, and observability across teams.

You will own end‑to‑end components, work with backend systems in Python, and collaborate closely with ML engineers, data scientists, and platform teams to advance model development and reliability.