Software Engineer, Machine Learning - Credit & Refund Optimization

DoorDash, Inc

Quick summary

Work type
On-site
Location
San Francisco, CA · Sunnyvale, CA · Seattle, WA
Salary
$137,100–$201,600 / yr
Posted
1 day ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $184k
This role $169k
$127k most similar roles pay here $236k

This role pays less than 64% of similar roles. Most pay $144,500–$224,325 — the shaded band above. At the midpoint, this role pays about $169k versus about $184k for comparable roles.

Based on 239 similar postings.

Employer

About DoorDash, Inc

DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States.

DoorDash, Inc currently has 238 open roles on FindRole.

Listed pay typically runs $131,600–$193,500 across 156 roles with salary data.

Most-posted roles

View all roles at DoorDash, Inc

At a glance

TL;DR · Software Engineer, Machine Learning - Credit & Refund Optimization

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As a Senior Machine Learning Engineer at DoorDash, you will join the team dedicated to enhancing customer satisfaction and retention through intelligent credit and refund systems. Your primary responsibilities include designing and deploying causal inference models to assess the impact of credits and refunds on user behavior, developing optimization frameworks that balance cost efficiency with customer experience, and building real-time personalized decision systems. You will collaborate closely with cross-functional teams to shape the product roadmap and lead end-to-end model development from experimentation to deployment. The ideal candidate has 3+ years of industry experience in machine learning, expertise in causal inference and optimization algorithms, proficiency in Python and tools like PyTorch, Spark, and MLflow, and a strong background in quantitative fields such as Computer Science or Statistics. This role addresses the complex challenge of optimizing user experiences at scale while maintaining operational efficiency.

Skills

Python PyTorch Spark MLflow Kubernetes CI/CD PostgreSQL AWS MLOps Docker Git Jupyter Notebook

What you'll do

  • Design and deploy causal inference models to assess the impact of refunds and credits.
  • Develop optimization frameworks balancing customer experience with operational costs.
  • Build real-time personalized decision systems adapting to user preferences.
  • Lead end-to-end model development, including experimentation and monitoring.
  • Collaborate on shaping product roadmaps for trust and consumer experience.

What we're looking for

  • 3+ years of industry experience in delivering impactful ML systems
  • Expertise in causal inference and statistical modeling techniques
  • Experience designing and deploying optimization algorithms for complex systems
  • Proficiency in Python and relevant ML tools like PyTorch, Spark, and MLflow
  • Strong product sense with ability to translate business objectives into technical solutions
  • M.S. or Ph.D. in a quantitative field such as Computer Science or Statistics

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