About the Company
DoorDash is a leading technology and logistics company committed to empowering local economies by connecting consumers with their favorite merchants. While best known for food delivery, DoorDash has evolved into a comprehensive platform delivering a wide range of goods and services to millions of users worldwide.
Our mission is to help local businesses thrive while providing convenient, reliable, and personalized experiences for customers. Through continuous innovation, rapid expansion, and community impact, DoorDash seeks passionate individuals who are ready to transform how people experience local commerce.
About the Role
We are seeking a Staff Machine Learning Engineer to join our dynamic team dedicated to developing intelligent and personalized systems that enhance fairness, efficiency, and trust on the DoorDash platform.
This role focuses on optimizing the credits and refunds experience—key elements influencing customer satisfaction and retention. You will lead the design, development, and deployment of advanced machine learning models that power refund and credit decision-making at scale.
Your work will directly impact millions of users weekly by ensuring an optimal balance between operational efficiency and customer satisfaction. Collaborating closely with cross-functional teams, including Engineering, Product, and Data Science, you will shape the strategic roadmap for trust, service recovery, and consumer engagement.
This is an excellent opportunity for an experienced ML engineer to apply expertise in causal inference, optimization, and data-driven personalization to drive meaningful business outcomes.
Key Responsibilities
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Design and deploy causal inference models to measure the impact of refunds and credits on customer satisfaction, retention, and behavior.
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Develop optimization frameworks that balance customer experience and operational costs within policy constraints.
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Build real-time personalized decision systems that adapt to customer behavior and platform trends.
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Partner with engineering, product, and data science teams to define and execute strategic initiatives for trust and consumer experience.
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Lead end-to-end model development including experimentation, deployment, and monitoring.
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Ensure all models are scalable, reliable, and compliant with internal and legal standards.
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Communicate complex technical insights to non-technical stakeholders and leadership.
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Stay current with industry research and integrate innovative techniques to improve performance.
Qualifications
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M.S. or Ph.D. in Computer Science, Statistics, Operations Research, Economics, Mathematics, or a related quantitative field.
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6+ years of experience developing and deploying impactful ML systems, particularly in personalization, optimization, or causal inference.
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Strong background in statistical modeling, causal inference, and techniques such as uplift modeling, treatment effect estimation, and instrumental variables.
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Experience designing optimization algorithms, including multi-objective and constrained optimization.
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Proficiency in Python and tools such as PyTorch, Spark, and MLflow.
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Strong product sense with the ability to connect technical solutions to business goals.
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Excellent communication and collaboration skills.
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Leadership mindset with humility, curiosity, and adaptability in fast-changing environments.
Benefits
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Competitive salary: $137,100 – $201,600 USD, based on experience and qualifications.
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Equity grants and ownership opportunities.
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Comprehensive medical, dental, and vision insurance.
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401(k) plan with employer matching.
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16 weeks of paid parental leave.
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Paid vacation and sick leave in compliance with applicable laws.
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Wellness, commuter, and reimbursement benefits.
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Disability and life insurance coverage.
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Family-forming support programs.
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Inclusive, growth-oriented work culture.
Equal Opportunity
DoorDash is an equal opportunity employer committed to fostering an inclusive workplace. We celebrate diversity and value each individual’s unique perspective. We do not discriminate on the basis of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or any other protected status.