Principal ML Engineer - Personalization & Production Systems

Walmart; Sunnyvale (CA); On-site; USD 169,000 - 338,000; Full time

Benefits offered by this job

Medical, vision and dental coverage

401(k)

Stock purchase plan

Paid time off

Job summary

Walmart's Personalization team in Sunnyvale, CA is seeking a Principal ML Engineer to design and build scalable production ML systems for customer experiences. You will own end-to-end ML lifecycle, from data pipelines to model deployment, with cross-functional partners across ML, software engineering, data science, product, and platform teams.

The role emphasizes mentorship, engineering excellence, and thoughtful trade-offs between model quality and production constraints like latency and cost.

Qualifications

• Bachelor’s degree in CS or related field with 5+ years in ML engineering.
• Experience building production ML systems and end-to-end ML lifecycle.
• Strong software engineering fundamentals and Python production practices.

Responsibilities

• Architect and lead scalable ML systems powering customer personalization.
• Own end-to-end ML lifecycle from data pipelines to deployment and monitoring.
• Design high-performance batch and real-time ML architectures at scale.
• Collaborate with Data Scientists and ML practitioners to productionize models.
• Mentor engineers and establish engineering best practices.

Skills

Python

ML systems

PyTorch

TensorFlow

CI/CD

Cloud platforms

Education

Bachelor’s degree in computer science or related field

Tools

PyTorch

TensorFlow

Scikit-learn

XGBoost

Job description

Walmart's Personalization team in Sunnyvale, CA is seeking a Principal ML Engineer to design and build scalable production ML systems for customer experiences. You will own end-to-end ML lifecycle, from data pipelines to model deployment, with cross-functional partners across ML, software engineering, data science, product, and platform teams.

The role emphasizes mentorship, engineering excellence, and thoughtful trade-offs between model quality and production constraints like latency and cost.