Job description

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 working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week). The national base pay range for this position within the United States, including Illinois and Colorado. $130,600 — $192,000 USD About DoorDash At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more. Our Commitment to Diversity and Inclusion We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel. Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination. Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation. If you need any accommodations, please inform your recruiting contact upon initial connection. Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We used Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provided Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023. We resumed using Covey Scout for Inbound again on June 29, 2024, and ceased using Covey Scout for Inbound on April 30, 2026. The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: getcovey.com . Beware of recruitment scams: DoorDash, Deliveroo, and Wolt will never ask you to pay money or share sensitive financial information during hiring — learn more about our legitimate recruiting process at Recruitment Scam Awareness - DoorDash .

Requirements

Must-haves

About the Team
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
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
Our Commitment to Diversity and Inclusion

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaipythonllmkubernetesawsgcpgo

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave

Wellness