Staff Machine Learning Engineer - DashPass

DoorDash, Inc

Actively hiring Posted today Verified listing
San Francisco, CA · Sunnyvale, CA Posted 1 day ago $137,100–$201,600 / year
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99+ other roles within 25 mi Near San Francisco, CA

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $215k
This role $169k
$122k most similar roles pay here $276k

This role pays less than 83% of similar roles. Most pay $179,851–$250,437 — the shaded band above. At the midpoint, this role pays about $169k versus about $215k for comparable roles.

Based on 240 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.

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View all roles at DoorDash, Inc

At a glance

TL;DR

As a Staff Machine Learning Engineer at DashPass, you will join a dynamic team focused on leveraging AI and advanced ML to enhance personalization efforts across the subscriber journey. Your primary responsibilities include designing and developing large-scale ML systems for causal inference modeling, incentive optimization frameworks, and budget allocation models to drive subscriber growth and retention. You will collaborate closely with Product, Data Science, and Engineering teams to implement production ML systems that directly impact key business metrics while providing technical mentorship to junior engineers. Ideal candidates have an M.S. or Ph.D. in a relevant field, 8+ years of industry experience building production-scale ML systems, expertise in Python, Java, C++, and frameworks like TensorFlow and PyTorch, and strong communication skills for cross-functional collaboration. This role is crucial for DoorDash’s strategic goal to grow its subscriber base through personalized offerings and efficient spend management.

Skills

Python TensorFlow PyTorch XGBoost Java C++ ML frameworks Probability theory Statistics Machine learning fundamentals CI/CD AWS Kubernetes Docker Prometheus Grafana

What you'll do

  • Develop and implement causal inference models to measure the impact of DashPass strategies.
  • Design incentive optimization frameworks for personalized progressive rewards to enhance spend efficiency.
  • Create budget allocation models to optimize spending across acquisition, referrals, and retention efforts.
  • Collaborate with cross-functional teams to design experiments and production ML systems impacting subscriber growth.
  • Build 0→1 ML systems that improve subscriber outcomes and overall marketplace health.
  • Set best practices for training, evaluating, deploying, and monitoring machine learning models.

What we're looking for

  • M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or related field.
  • 8+ years of industry experience building production-scale ML systems.
  • Strong understanding of probability theory, statistics, and machine learning fundamentals.
  • Proficient in Python, Java, or C++ with experience using TensorFlow, PyTorch, or XGBoost.
  • Proven ability to lead cross-functional initiatives and drive complex technical projects end-to-end.
  • Excellent communication skills for explaining technical concepts to various audiences.

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