Pay
Lower than most similar jobs
$131K–$192K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Software Engineer, ML Infrastructure (MDX)
San Francisco, CA; Sunnyvale, CA; Seattle, WA
Live, first seen by us 20 days ago- Pay
USD 130,600–192,000/yearAnnual period assumed — pay source
The national base pay range for this position within the United States, including Illinois and Colorado. $130,600—$192,000 USD About DoorDash
Read the full posting- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingYou will join a small, high-leverage team building the agentic layer of DoorDash's ML infrastructure.
You're excited about this opportunity because you will…
Design and build agent systems that propose, run, and evaluate modeling experiments
Build agent helpers for training, debugging pipelines, and reviewing deployments
Develop evaluation and observability for agent behavior
Make agent workflows reliable, with guardrails, cost controls, and monitoring
From the employer’s posting
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…
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
Work with production standards in mind, including reliability, safety guardrails, cost controls, monitoring, and clear playbooks
What you’ll bring
All qualificationsCore experience
- 2+ years of industry software engineering experience
- B.S., M.S., or PhD in Computer Science or equivalent
- Hands-on experience building ML infrastructure in production
- Strong backend fundamentals in Python and distributed systems
- Experience operating production systems with testing, observability, and reliability
Qualification wording
2+ years of industry experience in software engineering
B.S., M.S., or PhD in Computer Science or equivalent
Hands-on experience building ML infrastructure in production
Strong backend engineering fundamentals, especially in Python and distributed systems
Experience operating systems in production, including testing, observability, debugging, and reliability
Education & alternatives
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
About DoorDash USA
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.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 15, 202620 days ago
- Recorded sightings
- 108
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 20
Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
DoorDash USA is hiring
25 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is picking up
- Takes jobs down at a usual pace
- 4% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- AWS
- GCP
- Kubernetes
- MLflow
- Airflow
Where the posting names them
- 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
- 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 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)
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
The national base pay range for this position within the United States, including Illinois and Colorado. $130,600—$192,000 USD About DoorDash
- Location & working pattern
San Francisco, CA; Sunnyvale, CA; Seattle, WA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.