Staff ML Engineer: Real-Time Personalization & Optimization

DoorDash USA; Sunnyvale, San Francisco (CA, CA); On-site; USD 137,100 - 201,600; Full time

Benefits offered by this job

401(k) plan with employer matching

16 weeks of paid parental leave

Wellness benefits

Paid time off

Comprehensive medical, dental, and vision benefits

Job summary

A leading delivery technology company is seeking a Staff Machine Learning Engineer to design and develop large-scale ML systems aimed at personalizing the DashPass subscriber journey. This role involves building and deploying ML systems, optimizing incentive frameworks, and collaborating across multiple teams to enhance subscriber growth metrics. Candidates should possess an advanced degree in Computer Science and extensive industry experience in production-scale ML systems, along with excellent communication skills.

Qualifications

• 8+ years of industry experience building production-scale ML systems.
• Proven ability to lead cross-functional initiatives and drive complex projects.
• Strong understanding of statistics and machine learning fundamentals.

Responsibilities

• Contribute to causal inference modeling for subscriber acquisition strategies.
• Build and deploy ML systems to improve subscriber outcomes.
• Partner with Product and Data Science teams to design experiments.

Skills

Strong programming skills in Python

Strong understanding of probability theory

Experience with ML frameworks such as TensorFlow

Strong programming skills in Java or C++

Excellent communication skills

Education

M.S. or Ph.D. in Computer Science

Tools

TensorFlow

PyTorch

XGBoost

Job description

A leading delivery technology company is seeking a Staff Machine Learning Engineer to design and develop large-scale ML systems aimed at personalizing the DashPass subscriber journey. This role involves building and deploying ML systems, optimizing incentive frameworks, and collaborating across multiple teams to enhance subscriber growth metrics. Candidates should possess an advanced degree in Computer Science and extensive industry experience in production-scale ML systems, along with excellent communication skills.