Software Engineer, ML Infrastructure (MDX)
DoorDash USA · San Francisco, CA; Sunnyvale, CA; Seattle, WA
About this role
**Software Engineer, ML Infrastructure (MDX)** ## About the Team DoorDash’s MDX team sits within Machine Learning Platform and builds the shared foundation that ML engineers across DoorDash, Wolt, and Deliveroo rely on to build and run ML-powered products. You’ll work across the full ML lifecycle—feature engineering, training, evaluation, deployment, serving, and monitoring—with a focus on **agentic infrastructure** (systems that help agents accelerate and operate the ML workflow itself). ## About the Role Join a small, high-leverage team building the agentic layer of DoorDash’s ML infrastructure. You’ll design and build systems that let agents accelerate how ML engineers develop, ship, and operate models—covering: - **Auto research**: agents that explore and iterate on modeling ideas - **Agentic developer support**: help engineers move faster through training, evaluation, deployment, and debugging You’ll own well-scoped components end to end (design → production), working across agent orchestration, tooling/integrations with the ML platform, evaluation of agent behavior, and observability. This role is ideal if you have strong backend fundamentals and enjoy applying AI agents to real engineering workflows. ## You’re excited because you will… - Build agentic systems that increase the velocity of ML development across DoorDash, Wolt, and Deliveroo - Design and implement infrastructure for agents to propose, run, and evaluate modeling experiments - Build agentic developer support across the ML lifecycle (training setup, pipeline debugging, deployment review, platform navigation) - Develop evaluation and observability for agent behavior to measure impact, catch regressions early, and build trust for greater autonomy - Work with production standards (reliability, safety guardrails, cost controls, monitoring, clear playbooks) - Partner with ML engineers, data scientists, and platform teams to turn workflows into durable, reusable platform capabilities - Grow quickly in a fast-moving area and help shape how agents are used at scale ## We’re excited about you because you have… - B.S., M.S., or PhD in Computer Science (or equivalent) - 2+ years of industry software engineering experience - Hands-on experience building ML infrastructure in production (e.g., feature engineering/feature stores, ML data pipelines, training & experimentation platforms, evaluation, deployment/serving, monitoring) - Strong backend fundamentals, especially **Python** and **distributed systems** - Experience operating systems in production (testing, observability, debugging, reliability) - Understanding of how ML engineers and data scientists work; track record building platforms/tools/services that improve productivity - Ability to take ambiguous, well-scoped problems from idea to production and learn quickly - Proficiency using AI coding tools (e.g., Claude Code, Codex, Cursor) across the SDLC (design, code generation, testing, monitoring, releasing) ## Nice to Haves - Experience building LLM-powered applications/AI agents (tool use, orchestration, MCP servers) in production or substantial projects - Experience with agent evaluation, LLM observability, tracing, or building eval harnesses - Experience with ML platforms/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 with guardrails, safety, or co
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