Software Engineer, ML Infrastructure; MDX

Door Dash's MDX team sits within Machine Learning Platform and builds the shared foundation that ML engineers across Door Dash, 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.

You will join a small, high-leverage team building the agentic layer of Door Dash's ML infrastructure. You'll design and build the systems that let agents accelerate how ML engineers across Door Dash, 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.

You' re excited about this opportunity because…

• 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 and feature stores, data pipelines for ML, training and experimentation platforms, model evaluation, deployment, serving and inference, or monitoring
• Strong backend engineering fundamentals, especially in Python and distributed systems
• Experience operating systems in production, including testing, observability, debugging, and reliability
• A good understanding of how ML engineers and data scientists work, and a track record of building platforms, tools, or services that make them more productive
• Ability to take ambiguous, well‑scoped problems from idea to production, and to learn quickly in a fast‑moving technical area
• Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) across the software development lifecycle, including designing, generating code, testing, monitoring, and releasing software

• 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 (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 cost controls for LLM systems

The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job‑related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market‑dependent and may be modified in the…