Software Engineer, ML Infrastructure (MDX)

Build the agentic layer of DoorDash’s ML infrastructure and help ML engineers move from idea to production faster. On the MDX team, you’ll create shared platform capabilities that support the full ML lifecycle for ML engineers across DoorDash, Wolt, and Deliveroo, with a focus on reliability, safety guardrails, and operational excellence.

Work setup: Seattle, WA (onsite). Compensation: USD 130,600 - 192,000 per year.

What you’ll do

• Develop agentic systems that increase ML development velocity across DoorDash, Wolt, and Deliveroo, helping teams ship models sooner.
• Design and implement infrastructure that enables agents to propose, run, and evaluate modeling experiments.
• Create agent-driven developer support for the ML lifecycle, including agents that help engineers set up training jobs, debug failing pipelines, review model deployments, and navigate the platform.
• Build evaluation and observability for agent behavior to measure whether agents are helping, catch regressions early, and support trust as autonomy increases.
• Apply production standards to agentic workflows, including reliability, safety guardrails, cost controls, monitoring, and clear playbooks for operating against real ML systems.
• Partner with ML engineers, data scientists, and platform teams to understand where time is spent and convert repeated workflows into durable, reusable platform capabilities.
• Move quickly in a fast-moving area, shaping how agents are used to build and operate ML systems at company scale.

What you bring

• 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 feature engineering/feature stores, ML data pipelines, training and experimentation platforms, model evaluation, deployment, serving and inference, or monitoring
• Backend fundamentals, with strong experience in Python and distributed systems
• Experience operating systems in production, including testing, observability, debugging, and reliability
• Understanding of how ML engineers and data scientists work, plus a track record 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 include Claude Code, Codex, Cursor) across the development lifecycle, including designing, generating code, testing, monitoring, and releasing

Technologies you may work with

• 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 eval harnesses
• Experience with ML platforms or MLOps tooling (Kubeflow, MLflow, Ray, Airflow)
• Experience with Kubernetes and cloud infrastructure (AWS/GCP)
• Experience building developer tools, internal platforms, or self-serve infrastructure
• Experience with guardrails, safety, or cost controls for LLM systems

About the MDX team

The DoorDash MDX team sits within Machine Learning Platform and builds the shared foundation ML engineers across DoorDash, Wolt, and Deliveroo rely on to build and run ML-powered products. You’ll work across the full ML lifecycle, aiming to increase the velocity and reliability of ML-driven business impact. A key pillar is agentic infrastructure, including systems that enable agents to accelerate and operate the ML workflow, such as auto research (agents explore and iterate on modeling ideas) and self-healing systems (detect, diagnose, and remediate failures in ML pipelines and production models with minimal human intervention).