The posting, in DoorDash's own words
archived Oct 15, 2026About 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. We work across the full ML lifecycle: feature engineering, training, evaluation, deployment, serving, and monitoring. Our mission is to increase the velocity and reliability of ML-driven business impact across the company. A central pillar of that work is agentic infrastructure. We are building systems that let agents accelerate and operate the ML workflow itself, including 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.
About the Role
You will join a small, high-leverage team building the agentic layer of DoorDash's ML infrastructure. You'll design and build the systems that let agents accelerate how ML engineers across DoorDash, Wolt, and Deliveroo develop, ship, and operate models. That includes auto research systems that explore and iterate on modeling ideas, and agentic developer support that helps ML engineers move through training, evaluation, deployment, and debugging faster. You'll own well-scoped components end to end, from design through production, working across agent orchestration, tooling and integrations with our ML platform, evaluation of agent behavior, and observability. This role is ideal for an engineer with a strong backend foundation who is excited to apply AI agents to real engineering workflows, learns quickly, and wants to grow into technical leadership in a fast-moving area where model capabilities, tooling, and best practices change month to month.
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You're excited about this opportunity because you will…
Build agentic systems that increase the velocity of ML development across DoorDash, Wolt, and Deliveroo, helping ML engineers get from idea to production model faster. Design and implement infrastructure that lets agents propose, run, and evaluate modeling experiments. Build developer support for the ML lifecycle, such as agents that help engineers set up training jobs, debug failing pipelines, review model deployments, and navigate our platform. Develop evaluation and observability for agent behavior, so we can measure whether agents are actually helping, catch regressions early, and build the trust needed to give them more autonomy. Work with production standards in mind, including reliability, safety guardrails, cost controls, monitoring, and clear playbooks, so that can run against real ML systems. Partner closely with ML engineers, data scientists, and platform teams to understand where their time goes and turn those workflows into durable, reusable platform capabilities. Grow quickly in a fast-moving area, with the chance to shape how agents are used to build and operate ML systems at a company of this scale.