Staff Machine Learning Engineer, Real-Time Personalization

DoorDash; San Francisco, Sunnyvale (CA, CA); On-site; USD 137,000 - 299,000; Full time

Benefits offered by this job

401(k) matching

Paid parental leave

Healthcare benefits

Job summary

DoorDash is seeking a Staff Machine Learning Engineer in San Francisco to lead the design and development of large-scale ML/optimization systems powering personalization for the DashPass journey.

The role requires an MS/PhD, 8+ years of production ML experience, and strong skills in Python/Java/C++, TensorFlow, PyTorch and XGBoost. You will guide experiments, production ML frameworks, and mentor engineers across product, data science and engineering.

Qualifications

• MS/PhD in Computer Science, Machine Learning, Statistics or related field.
• 8+ years of industry experience building production-scale ML systems.
• Proficiency with AI coding tools and full software lifecycle.
• Strong knowledge of probability, statistics and ML fundamentals.
• Strong programming skills in Python, Java, or C++, with ML frameworks.

Responsibilities

• Design and develop large-scale ML/optimization systems for personalization.
• Lead cross-functional initiatives across product, data science and engineering.
• Set best practices for model training, evaluation, deployment and monitoring.
• Provide technical mentorship to engineers and partners.
• Build and deploy 0→1 ML systems that improve subscriber outcomes.

Skills

AI coding tools

Python/Java/C++

Probability & statistics

Cross-functional collaboration

Communication

Education

MS/PhD in CS/ML/Stats

Tools

TensorFlow

PyTorch

XGBoost

Job description

DoorDash is seeking a Staff Machine Learning Engineer in San Francisco to lead the design and development of large-scale ML/optimization systems powering personalization for the DashPass journey.

The role requires an MS/PhD, 8+ years of production ML experience, and strong skills in Python/Java/C++, TensorFlow, PyTorch and XGBoost. You will guide experiments, production ML frameworks, and mentor engineers across product, data science and engineering.