Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Principal Machine Learning Engineer, TEAM role.

Rezi rewrites your resume against DoorDash USA's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Principal Machine Learning Engineer, TEAM posting at DoorDash USA — free, in seconds.

About the Role

DoorDash is building the next generation of causal decisioning systems for emerging categories. This role leads the Causal ML pod, establishing the technical foundation for company-level causal decisioning. The central mandate is to define and build a durable company-level causal value metric to estimate incremental value from product, growth, and marketplace actions.

Responsibilities

Set the multi-year technical direction for causal ML, lead the pod’s portfolio and operating model, and remain close to the hardest modeling and systems work.
Define the company-level causal value metric and its measurement framework.
Build the metric into a decision system for product prioritization, experiment readouts, intervention selection, budget allocation, and portfolio tradeoffs.
Establish how randomized experiments, quasi-experiments, observational estimation, and learned models work together.
Architect reusable causal capabilities for treatment effect estimation, surrogate validation, counterfactual policy evaluation, sensitivity analysis, and long-term outcome forecasting.
Guide production applications across promotions, lifecycle interventions, ranking, recommendations, search, substitutions, demand shaping, and inventory-aware discovery.
Set standards for validation, monitoring, reproducibility, and governance.
Influence senior leaders across Product, Engineering, Analytics, Finance, Strategy, and business teams.
Develop senior engineers and scientists through technical direction, design review, coaching, and a high bar for causal reasoning and engineering craft.

Requirements

Extensive experience (10+ years) in causal inference, econometrics, experimentation, or causal machine learning, with a record of setting technical direction beyond a single team.
Experience leading the design and productionization of causal models, measurement platforms, experimentation systems, or large-scale decision engines.
Deep judgment about randomized experiments, observational methods, surrogate endpoints, and model-based decisioning, including when evidence is insufficient for a decision.
Strong ML engineering and systems ability.
The ability to reason about long-term customer and marketplace value, not only local model metrics or immediate conversion.
A track record of influencing executives and senior cross-functional partners through clear problem framing, technical judgment, and evidence.
A multiplier mindset: you create reusable abstractions, improve decision quality across teams, and raise the technical standard of the people around you.

Skills

Doubly robust estimation
Double machine learning
Instrumental variables
Difference-in-differences
Synthetic controls
Variance reduction
Heterogeneous treatment effects
Contextual bandits
Off-policy evaluation
ML engineering
Systems ability
Data contracts
Modeling pipelines
Evaluation frameworks
Serving patterns
Monitoring

Location

United States
Illinois
Colorado
Remote

Work Type

Full-time

Experience Level

Principal
10+ years

Salary/Compensations

$282,100—$414,800 USD

Benefits

Opportunities for equity grants
401(k) plan with employer matching
16 weeks of paid parental leave
Wellness benefits
Commuter benefits match
Paid time off
Paid sick leave
Medical benefits
Dental benefits
Vision benefits
11 paid holidays
Disability and basic life insurance
Family-forming assistance
Mental health program
Flexible paid time off/vacation (for salaried roles)
80 hours of paid sick time per year (for salaried roles)

About the Company

DoorDash's mission is to empower local economies.
DoorDash is a technology and logistics company.
DoorDash is growing rapidly and changing constantly.
DoorDash is committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks.

Equal Opportunity

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities.
We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.
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.
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.
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.