DoorDash
Technology
SoftwareEngineer,MachineLearning-Credit&RefundOptimization
Neural analysis suggests this role is
optimal for Mid candidates.
“Software Engineer, Machine Learning - Credit & Refund Optimization at DoorDash. Skills: Machine Learning, Causal Inference, Optimization. Designing and deploying causal inference models. Developing optimization frameworks”
What You'll Achieve.
Drive fairness, efficiency, and trust in the DoorDash platform; Optimize and personalize credits and refund decisions at scale; Balance cost efficiency with long-term customer retention and experience; Influence millions of user experiences every week
Industry & Context.
Solving new challenges
What They're Looking For.
Must Have
3+ years of industry experience delivering machine learning systems with clear business impact, Deep expertise in statistical modeling and causal inference (e. g. , uplift modeling, treatment effect estimation, synthetic controls, instrumental variables), Experience designing and deploying optimization algorithms (e. g. , multi-objective optimization, bandits, constrained optimization), Proficiency in Python, M.S. or Ph. D. in a quantitative field (e. g. , Computer Science, Statistics, Operations Research, Economics, Mathematics), Excellent communication skills
Nice to Have
Experience in personalization, optimization, or causal inference, ML tooling such as PyTorch, Spark, and MLflow, A product sense and ability to translate business objectives into technical solutions, Track record of cross-functional leadership
What You'll Do.
Designing and deploying causal inference models
Developing optimization frameworks
Building personalized decision systems
Leading end-to-end model development
How You'll Work.
Team & Collaboration
Collaborating with engineering, product, and data science partners; Partnering with cross-functional leaders
Communication Scope
Excellent communication skills
Full Job Description
About the Team Join the team focused on building intelligent, personalized systems that drive fairness, efficiency, and trust in the DoorDash platform. We own the credits and refunds experience—key components of customer satisfaction and retention—and we’re pioneering new ways to optimize and personalize these decisions at scale using causal inference and optimization. About the Role We're seeking a Machine Learning Engineer to lead the development of state-of-the-art ML systems that personalize and optimize credits and refund decisions. This work is critical to balancing cost efficiency with long-term customer retention and experience. In this high-impact role, you will partner with cross-functional leaders to design and deploy causal models and optimization algorithms that influence millions of user experiences every week. You’re excited about this opportunity because you will… Designing and deploying causal inference models to accurately assess the impact of refunds and credits on customer satisfaction, retention, and behavior Developing optimization frameworks that balance customer experience with operational cost, under policy and budget constraints Building personalized decision systems that adapt to customer preferences and platform dynamics in real time Collaborating with engineering, product, and data science partners to shape the roadmap for trust, service recovery, and consumer experience Leading end-to-end model development, including experimentation, deployment, monitoring, and iteration We’re excited about you because you have: 3+ years of industry experience delivering machine learning systems with clear business impact, especially in personalization, optimization, or causal inference Deep expertise in statistical modeling and causal inference (e.g., uplift modeling, treatment effect estimation, synthetic controls, instrumental variables) Experience designing and deploying optimization algorithms (e.g., multi-objective optimization, bandits, constrain
Applying for this Software Engineer, Machine Learning - Credit & Refund Optimization role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Greenhouse
- Create a Greenhouse profile before applying — it saves time across multiple applications.
- Upload your resume as a PDF; the parser handles it better than Word.
- Answer all knockout questions carefully — wrong answers auto-reject before a human sees you.
- Enable email notifications to track application status in real time.
ANONYMOUS · UNFILTERED
What do employees actually say about DoorDash?
Real rants from real employees. Read before you apply.