This role pays less than
85%
of similar roles. Most pay
$194,300–$256,950
— the shaded band above.
At the midpoint, this role pays about
$169k
versus about
$226k
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
187 open roles
on FindRole.
Listed pay typically runs
$144,800–$212,950
across 166 roles with salary data.
Staff Machine Learning Engineer, Fulfillment Planning joins the Fulfillment Planning team to build intelligence powering the logistics network. This role involves designing, developing, and deploying large-scale production machine learning systems that drive real-time decisioning for assignment, routing, batching, timing, and fulfillment estimation across various business lines. The engineer will own foundational ML systems, establish modeling and deployment standards, and mentor others while defining technical direction for high-reliability, low-latency Tier-0 services. Candidates must be fluent in Python and possess experience with modern deep learning frameworks to solve complex problems like demand shaping and knowledge distillation from large teacher models into efficient production versions. The work focuses on the core logistics problem of optimizing delivery execution to improve merchant outcomes and Dasher efficiency while reducing costs across diverse geographies and specialized delivery types including grocery, retail, and parcel.
What does a Machine Learning Engineer earn in California?
Median $246394 from 172 postings across 28 companies.
Machine Learning
Python
Golang
TensorFlow
PyTorch
Hugging Face
LLM
GenAI
RAG
Spark
Kafka
Ray
Airflow
BigQuery
Redis
Distributed Systems
NLP
Search Systems
Recommender Systems
Machine Learning
LLM Fine-tuning
Agentic Workflows
Python
Recommendation Systems
Deep Learning
Feature Engineering
ML Infrastructure
Model Deployment
Monitoring
Data Engineering
Optimization