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# Software Engineer, Machine Learning - Credit...

- **Company:** DOORDASH, INC.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $137,100.0 - $201,600.0
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Pytorch, Apache Spark, Information Technology, Optimization Algorithms, Machine Learning Operations
- **Published:** May 17, 2026
- **Apply:** https://www.juju.com/job/00000000g06u3g

## About the Role

+ 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, constrained optimization)

 + Proficiency in Python and ML tooling such as PyTorch, Spark, and MLflow

 + A strong product sense and ability to translate business objectives into technical solutions

 + M.S. or Ph.D. in a quantitative field (e.g., Computer Science, Statistics, Operations Research, Economics, Mathematics)

 + Excellent communication skills and a track record of cross-functional leadership

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

## Description

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


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