Staff Machine Learning Engineer

About the Role

The Staff Machine Learning Engineer role at DoorDash centers on designing and implementing large-scale machine learning and optimization systems that power personalization across the DashPass subscriber lifecycle. You will own the development of causal inference models that quantify the impact of acquisition and retention strategies, enabling data-driven decisions on promotions and incentives. You will build incentive optimization frameworks that tailor progressive rewards to improve spend efficiency and long-term subscriber value. This position requires close collaboration with product, data science, and engineering teams to translate business objectives into scalable model frameworks and production systems. You will mentor engineers and cross-functional partners, leading through technical influence rather than direct management. The focus is on delivering zero-to-one ML systems that enhance subscriber outcomes and overall marketplace health while establishing best practices for the full model lifecycle.

What You'll Do

• Architect and deploy large-scale ML and optimization systems that personalize the DashPass subscriber journey in real time.
• Develop causal inference models to measure incremental impact of subscriber acquisition, retention, and churn reduction initiatives.
• Design incentive optimization frameworks that personalize progressive rewards to improve spend efficiency and lifetime value.
• Build budget allocation and forecasting models that identify optimal spend across acquisition, referrals, and retention levers.
• Partner with product, data science, and engineering teams to design experiments, model frameworks, and production ML systems that directly influence subscriber growth metrics.
• Provide technical mentorship and cross-functional guidance, driving complex technical projects end-to-end through influence and leadership.
• Implement 0-to-1 ML systems that improve subscriber outcomes, marketplace health, and operational efficiency.
• Establish and maintain best practices for model training, evaluation, deployment, monitoring, and continuous improvement.
• Leverage AI coding tools such as Claude Code, Codex, and Cursor throughout the software development lifecycle, from design and code generation to testing and release.
• Ensure models are robust, scalable, and aligned with business objectives while supporting rapid experimentation and iteration.

Requirements

• Hold an M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a closely related quantitative field.
• Bring 8+ years of industry experience building production-scale ML systems that are deployed in live environments.
• Demonstrate proficiency in using AI coding tools such as Claude Code, Codex, and Cursor across the full software development lifecycle, including design, code generation, testing, monitoring, and releasing software.
• Show a strong understanding of probability theory, statistics, and machine learning fundamentals, including supervised and unsupervised learning methods.
• Possess strong programming skills in Python, Java, or C++, along with hands-on experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost.
• Exhibit interest in building and leading a new team that has broad impact across multiple problem spaces in support of a critical business line.
• Prove ability to lead cross-functional initiatives and drive complex technical projects from conception through production deployment.
• Communicate effectively with product, business, and engineering audiences, translating technical concepts into clear and actionable insights.
• Have prior experience in subscriptions growth or marketplace systems, which is noted as a valuable background for this role.

Notice Regarding Use of AI and Automated Tools

To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem. How it works: your application data may be processed by Gem, an automated decision-making tool, to match your profile to relevant opportunities and route your application through our hiring workflow. Automated decision-making tools may also be used to assess, screen, or evaluate your application. If you do not consent to the processing of your application data through these automated tools, you may withdraw your application at any time. By proceeding with your application, you acknowledge and consent to such processing of your application data. If you have any questions or concerns about this notice or wish to exercise your data protection rights, please contact careers@doordash.com.