Principal Machine Learning Engineer, Causal Decisioning

DoorDash, Inc.; San Francisco (CA); Vor Ort; USD 282.000 - 415.000; Vollzeit

Benefits dieser Stelle

401(k) plan with employer matching

Paid parental leave

Medical, dental & vision benefits

11 paid holidays

Zusammenfassung

DoorDash seeks a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for causal decisioning. You will define company‑level causal value, align experiments with production systems, and guide impactful product decisions across promotions and discovery.

The role emphasizes long-term value estimation, exploration of surrogate metrics, and governance to ensure reliable causal estimates amid changing marketplaces.

Qualifikationen

• Extensive experience (10+ years) in causal inference, econometrics, experimentation, or causal machine learning.
• 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.
• Fluency with advanced causal methods and when evidence is insufficient for a decision.

Aufgaben

• Set multi-year technical direction for causal ML and lead the pod’s portfolio and operating model.
• Define the company-level causal value metric and measurement framework.
• Build the metric into a decision system for prioritization and budgeting.
• Architect reusable causal capabilities for treatment effect estimation and counterfactual evaluation.
• Guide production applications across promotions, lifecycle interventions, and ranking.

Kenntnisse

Causal inference

Econometrics

Experimentation

Causal machine learning

Productionization

Measurement platforms

Experimentation systems

Decision engines

Data contracts

Modeling pipelines

Monitoring

Long-term value reasoning

Tools

Doubly robust estimation

Double machine learning

Instrumental variables

Difference-in-differences

Synthetic controls

Variance reduction

Heterogeneous treatment effects

Contextual bandits

Off-policy evaluation

Jobbeschreibung

About the Team

DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, dynamic marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question.

We are hiring a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-level causal decisioning. This is a senior technical leadership role for a practitioner who has built consequential causal systems in production and can turn ambiguous business questions into a coherent measurement and decision platform.

A central mandate is to define and build a durable company-level causal value metric: a trusted, long‑term signal that estimates the incremental value created by product, growth, and marketplace actions. The metric will connect experiments, observational evidence, and production ML so leaders and product teams can compare investments on a common basis while protecting customer experience and marketplace health.

About the Role

You will 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. You will be accountable for both scientific credibility and production impact.

You’re excited about this opportunity because you will…

• Lead the Causal ML pod across technical strategy, architecture, execution, and quality. Create a roadmap that joins foundational platform work with high-value product applications.
• Define the company-level causal value metric and its measurement framework, including the target construct, time horizon, component outcomes, identification strategy, calibration, uncertainty, and guardrails.
• Build the metric into a decision system that teams can use 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. Make the limits of each source of evidence explicit.
• 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 so causal estimates remain reliable as policies, populations, and marketplace conditions change.
• Influence senior leaders across Product, Engineering, Analytics, Finance, Strategy, and business teams by translating complex causal evidence into clear decisions and tradeoffs.
• Develop senior engineers and scientists through technical direction, design review, coaching, and a high bar for causal reasoning and engineering craft.

Example focus areas include:

• Company‑level causal value: Create a common, causally grounded measure of the long‑term value generated by product and business actions, enabling teams to compare opportunities across surfaces while preserving interpretable components and guardrails;
• Consumer action and lifetime value: Estimate which interventions truly grow durable customer value rather than pull demand forward, including promotions, lifecycle nudges, personalization, retention, and reactivation;
• Surrogate metrics and faster learning: Develop and validate early indicators that accelerate decisions before long‑term outcomes mature, paired with variance reduction, sequential learning, and clear standards for trust;
• Counterfactual ranking and decisioning: Build causal layers for recommendations, search, targeting, demand shaping, and marketplace allocation so systems optimize incremental outcomes rather than predictive relevance alone; and
• Causal measurement platform: Create shared infrastructure that connects experiments, observational data, policy logs, estimation, calibration, and decision workflows across teams.

We’re Excited About You Because You Have…

• 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.
• Fluency with methods such as doubly robust estimation, double machine learning, instrumental variables, difference‑in‑differences, synthetic controls, variance reduction, heterogeneous treatment effects, contextual bandits, and off‑policy evaluation.
• Strong ML engineering and systems ability. You can shape data contracts, modeling pipelines, evaluation frameworks, serving patterns, and monitoring for high‑stakes production use.
• 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.

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).

$282,100 — $414,800 USD

The national base pay range for this position within the United States, including Illinois and Colorado.

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.

Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Metaview.

How it works: Metaview assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Metaview or other AI-enabled tool in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy https://help.doordash.com/en-us/legal/article/ax-privacy-notice and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Metaview is publicly available for review at https://trust.warden-ai.com/metaview/application-review.