Staff Data Scientist- MLE – Recommendations & Personalization

Walmart

Quick summary

Work type
On-site
Location
Sunnyvale, CA
Salary
$143,000–$286,000 / yr
Posted
171 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $174k
This role $214k
$99k most similar roles pay here $306k

This role pays more than 72% of similar roles. Most pay $129,200–$219,612 — the shaded band above. At the midpoint, this role pays about $214k versus about $174k for comparable roles.

Based on 240 similar postings.

Employer

About Walmart

Walmart Inc. is the world''s largest retailer by revenue, operating a chain of hypermarkets, discount department stores, and grocery stores, as well as a growing e-commerce presence through Walmart.com. Industry: General Merchandise & Grocery Retail

Walmart currently has 529 open roles on FindRole.

Listed pay typically runs $117,000–$234,000 across 523 roles with salary data.

Most-posted roles

View all roles at Walmart

At a glance

TL;DR · Staff Data Scientist- MLE – Recommendations & Personalization

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We are seeking a Staff Engineer to lead the feature engineering ecosystem for large-scale recommendation and personalization systems at Walmart Global Tech. This role involves architecting end-to-end feature architecture, designing feature stores optimized for recsys workloads, and defining standards for versioning and lineage. The ideal candidate will drive technical leadership by influencing best practices across multiple teams, mentoring senior engineers, and reviewing complex designs. Responsibilities also include enabling cross-surface feature reuse, ensuring observability, and handling incident response for critical pipelines impacting revenue and engagement. Candidates should have 4+ years of experience in data engineering or recsys platforms, expertise in distributed systems, and strong technical communication skills.

Skills

Python Java Kafka Hadoop Spark PostgreSQL Redis Docker Kubernetes Terraform CI/CD Prometheus Grafana Git AWS Azure Bigtable Cassandra Feature Store MLflow

What you'll do

  • Design end-to-end feature architecture for recommendation systems.
  • Lead the evolution of offline and online feature stores optimized for recsys workloads.
  • Define standards for feature versioning, lineage, and point-in-time correctness.
  • Mentor senior engineers on scalable data systems and recsys feature design.
  • Enable cross-surface feature reuse across multiple recommendation surfaces.

What we're looking for

  • 4+ years of experience in data engineering, ML engineering, or recsys platform roles.
  • Deep expertise in distributed systems and large-scale data processing.
  • Proven track record of architecting feature platforms for recommendation systems.
  • Strong understanding of ranking architectures, candidate generation, and personalization patterns.
  • Exceptional technical communication and leadership skills to mentor senior engineers.

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