Software Engineer, ML Infrastructure (MDX)

DoorDash is hiring a Software Engineer for ML Infrastructure to build the agentic layer of its machine learning platform. In this role, you will help ML teams across DoorDash, Wolt, and Deliveroo develop, ship, and operate models more quickly through orchestration, tooling integrations, and reliable evaluation and observability.

Responsibilities

• Build agentic systems that increase the velocity of ML development across DoorDash, Wolt, and Deliveroo, enabling engineers to move from idea to production models faster.
• Design and implement infrastructure that allows agents to propose, run, and evaluate modeling experiments.
• Create agent-driven developer support across the ML lifecycle, including helping engineers set up training jobs, debug failing pipelines, review model deployments, and navigate the platform.
• Develop evaluation and observability for agent behavior to measure whether agents are making meaningful improvements, detect regressions early, and support trust and increased autonomy.
• Implement production-minded workflows with reliability, safety guardrails, cost controls, monitoring, and clear playbooks so agentic operations work with real ML systems.
• Collaborate closely with ML engineers, data scientists, and platform teams to understand where time is spent and convert those workflows into durable, reusable platform capabilities.
• Operate effectively in a fast-moving area, helping shape how agents are used to build and operate ML systems at company scale.

Requirements

• B.S., M.S., or PhD in Computer Science or equivalent.
• 2+ years of industry experience in software engineering.
• Hands-on experience building ML infrastructure in production, including at least some of: feature engineering and feature stores, ML data pipelines, training and experimentation platforms, model evaluation, deployment and serving/inference, and monitoring.
• Strong backend fundamentals, with particular emphasis on Python and distributed systems.
• Production experience operating systems with testing, observability, debugging, and reliability.
• Understanding of how ML engineers and data scientists work, with a track record of building platforms, tools, or services that improve productivity.
• Ability to take ambiguous but well-scoped problems from idea to production and learn quickly in a fast-moving technical area.
• Proficiency using AI coding tools (examples: Claude Code, Codex, Cursor) across the software development lifecycle, including designing, generating code, testing, monitoring, and releasing software.

Technologies

• Python
• Claude Code
• Codex
• Cursor
• Kubeflow
• MLflow
• Ray
• Airflow
• Kubernetes
• AWS
• GCP
• MCP

Nice to Have

• Experience building LLM-powered applications or AI agents, including tool use, orchestration, or MCP servers, in production or on substantial projects.
• Experience with agent evaluation, LLM observability, tracing, or building evaluation harnesses.
• Experience with ML platforms or MLOps tooling (e.g., Kubeflow, MLflow, Ray, Airflow).
• Experience with Kubernetes and cloud infrastructure (AWS/GCP).
• Experience building developer tools, internal platforms, or self-serve infrastructure.
• Experience adding guardrails, safety mechanisms, or cost controls for LLM systems.

About the Team

The DoorDash MDX team operates within Machine Learning Platform and builds shared capabilities that ML engineers across DoorDash, Wolt, and Deliveroo rely on for building and running ML-powered products. The team works across the full ML lifecycle, including feature engineering, training, evaluation, deployment, serving, and monitoring, with the mission to increase the velocity and reliability of ML-driven business impact.

A key pillar of the work is agentic infrastructure, including systems that enable agents to accelerate and operate the ML workflow. This includes auto research, where agents explore and iterate on modeling ideas, and self-healing systems that detect, diagnose, and remediate failures in ML pipelines and production models with minimal human intervention.

Location and Compensation

• Location: Seattle, WA (onsite)
• Salary: USD 130,600 - 192,000 per yearly