Software Engineer, ML Infrastructure (MDX)

Role details

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. 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. 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 agentic 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 agentic workflows 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. We're excited about you 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 Nice To Haves 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 Compensation 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 future. In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information. DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others. To learn more about our benefits, visit our careers page here . See below for paid time off details: For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year. For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if worki