Staff Machine Learning Engineer, Fulfillment Planning

DoorDash, Inc

Quick summary

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

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $215k

This role $169k

$122k most similar roles pay here $275k

This role pays less than 81% of similar roles. Most pay $183,668–$246,150 — 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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At a glance

TL;DR · Staff Machine Learning Engineer, Fulfillment Planning

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As a Staff Machine Learning Engineer at DoorDash’s Fulfillment Planning team, you will lead the design and deployment of large-scale production ML systems that drive real-time decision-making across the logistics ecosystem. Your daily tasks include building foundational ML systems for assignment and fulfillment estimation, working on complex real-world problems like real-time routing and ETA prediction, and defining how machine learning is applied to various delivery types such as grocery and retail. You will establish best practices for model development and governance while collaborating closely with cross-functional teams including Product, Data Science, and Platform Engineering. Ideal candidates have over 8 years of experience in building production-scale ML systems, fluency in Python, hands-on expertise with deep learning frameworks, and a track record of launching mission-critical models in production environments. This role offers the opportunity to shape DoorDash’s AI vision for logistics by creating an LLM-inspired foundation model that enhances intelligence across all fulfillment products.

Skills

Python TensorFlow PyTorch Kubernetes AWS CI/CD MLOps Prometheus Grafana PostgreSQL Redis Docker Git Scikit-learn Pandas Jupyter ELK_stack Kafka Spark

What you'll do

Own and build foundational ML systems that impact delivery quality, cost, and logistics efficiency.
Lead 0→1 ML initiatives to define how machine learning is applied across fulfillment products.
Work on challenging real-world problems like real-time assignment, routing, and fulfillment estimation.
Establish best practices for model development, deployment, monitoring, retraining, and governance.
Mentor other engineers and raise the technical bar for logistics ML across DoorDash.

What we're looking for

8+ years of experience building and deploying production-scale machine learning systems.
Strong foundation in machine learning with application to large-scale production systems.
Fluency in Python and hands-on experience with modern ML frameworks, especially deep learning.
Proven track record of designing, launching, and operating mission-critical ML models/systems.
Ability to lead complex technical projects end-to-end and influence stakeholders across teams.
Experience building or shipping large-scale ML models for recommendation, ads, marketplace, logistics, etc.
Knowledge in distilling knowledge from large teacher models into efficient production models.

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Python TensorFlow PyTorch Kubernetes AWS CI/CD PostgreSQL Docker LLM systems JSON APIs RESTful services Scalability Performance optimization Deep learning Fine-tuning

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