Applied ML Scientist - Personalization & Recommender Systems

Walmart Global Tech; Sunnyvale (CA); On-site; USD 143,000 - 286,000; Full time

Benefits offered by this job

Stock options

Bonus program

Health benefits

Paid time off

Job summary

Walmart Global Tech in Sunnyvale, CA is seeking an applied scientist specializing in Recommender Systems, Information Retrieval, and Reinforcement Learning. You will invent scalable AI solutions, push the boundaries with LLMs and deep models, and impact millions of customers across Walmart’s platforms.

The role emphasizes production-grade modelling, fast iteration, and collaboration with the Personalization team to improve shopper experiences across e-commerce touchpoints.

Qualifications

• PhD with 2+ years or MS with 5+ years in CS/ML/Statistics or OR
• Experience implementing and scaling production-grade ML models
• Strong background in reinforcement learning and contextual bandits
• Proficiency in Java, Python, PySpark; TensorFlow/PyTorch

Responsibilities

• Map product and business requirements to an ML/AI problem
• Research, design and implement scalable ML models for recommender systems
• Analyze large datasets to derive actionable insights
• Build robust ML modelling systems with LLMs, DL and advanced statistics
• Collaborate cross-functionally to align product goals

Skills

Machine Learning

Reinforcement Learning

Contextual Bandits

Python

Java

PySpark

TensorFlow

PyTorch

Data Analysis

Dimensionality Reduction

Education

PhD in CS/ML

MS in CS/Math/Analytics

Tools

Spark

PySpark

Scikit-learn

Job description

Walmart Global Tech in Sunnyvale, CA is seeking an applied scientist specializing in Recommender Systems, Information Retrieval, and Reinforcement Learning. You will invent scalable AI solutions, push the boundaries with LLMs and deep models, and impact millions of customers across Walmart’s platforms.

The role emphasizes production-grade modelling, fast iteration, and collaboration with the Personalization team to improve shopper experiences across e-commerce touchpoints.