Principal Machine Learning Engineer, TEAM

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info, not invent or summarize away detail. Remove boilerplate like EEO, legal, etc. Also remove duplicated lines, application instructions. We need to parse the posting: It has sections: About The Team, About The Role, bullet points under "You will…", "Example Focus Areas Include", "We’re Excited About You Because You Have…", Compensation, Benefits, About DoorDash, etc. We need to map to headings: About the role (maybe combine About The Team and About The Role? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must only include sections when posting has real specific content. We have content about role: "About The Role" paragraph and bullet list of responsibilities (You will…). That fits About the role and Responsibilities. We have qualifications: "We’re Excited About You Because You Have…" includes experience, skills, etc. That could be Requirements, Qualifications, Skills. We need to decide how to split. We have compensation: "Compensation" paragraph with pay range and equity. That's Pay. We have benefits: benefits paragraph with details. That's Benefits. Schedule: Not mentioned. So we should not include Schedule heading. We also have About The Team maybe could be part of About the role? But we can include About the role covering both team and role. We must not include boilerplate like DoorDash mission, Diversity, etc. Remove those. We must preserve all substantive info: need to keep details like bullet points, examples, compensation range, benefits specifics. We must not invent or summarize away detail: keep as is. We need to output only HTML fragment, no /, no markdown fences. Thus produce something like:

About the role

... (combined About The Team and About The Role paragraphs)

Responsibilities

• Lead the Causal ML pod across technical strategy, architecture, execution, and quality.
...

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.
...

Maybe also Qualifications and Skills separate? Could combine into Requirements. But instruction says prefer few substantive sections over many thin ones. So we could have Requirements covering experience, skills, judgment, etc. Could also have Skills separate if distinct. But we can keep one Requirements section covering all. We need Benefits section: list benefits. Pay section: include pay range and equity. We must not include placeholder like "commensurate with experience". Not present. Let's extract specifics. From 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." From 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." Then Example Focus Areas Include list: we could treat as part of responsibilities or maybe as separate but we can embed in responsibilities. We need to preserve all substantive info; we can include them as bullet points under Responsibilities. Now "We’re Excited About You Because You Have…" list: - 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. We can put these under Requirements. 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. $282,100—$414,800 USD" We need Benefits section: list benefits: 401(k) with employer matching, 16 weeks paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave (compliance), medical, dental, vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, mental health program, plus PTO details. We should not include the "Talk to your recruiter for more information." maybe that's application instruction? It's not exactly instruction to apply but it's about compensation. Might keep? It's not boilerplate; it's part of compensation description. Could keep. Pay section: include the pay range and equity grants. We must not include "About DoorDash" mission etc. Remove. We must not include Diversity, EEO, etc. Now produce HTML. Structure:

About the role

... (combined About The Team and About The Role paragraphs)

Responsibilities

• 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