Staff, Software Engineer - Backend / ML

Walmart · Retail · Sunnyvale, CA

Staff Software Engineer focused on backend microservices, data pipelines, and ML-serving infrastructure for large-scale search at Walmart. This role involves technical leadership, architectural design, and hands-on development of high-throughput, low-latency systems, including ML model productionization and MLOps.

What you'd actually do

  1. Define the technical direction and drive the architecture for mission-critical search microservices — spanning Core Orchestration, Query Understanding, Autocomplete, Facet & Navigation, Ranking, and more.
  2. Design, build, and optimize high-throughput, low-latency backend services, applying best practices around distributed systems, fault tolerance, horizontal scalability, concurrency, and performance tuning.
  3. Design and build high-scale data and feature pipelines that process data through transformation and aggregation layers into downstream data stores, search indices, and feature stores.
  4. Architect complex query patterns and integrations with search engines to power relevance, ranking, and retrieval at scale.
  5. Lead discovery and design phases for medium-to-large initiatives — partnering with product management, data science, and UX to translate business requirements into scalable technical solutions; build cross-functional alignment, drive proof-of-concepts, and validate ideas through prototypes.

Skills

Required

  • Java
  • Spring Boot
  • object-oriented design
  • concurrency
  • performance optimization
  • cloud-native microservices
  • RESTful API design
  • fault tolerance patterns
  • horizontal scaling
  • Kubernetes
  • Apache Spark
  • SparkSQL
  • Kafka
  • NoSQL databases
  • Cassandra
  • technical leadership
  • test-driven development
  • testing frameworks
  • JUnit
  • Mockito
  • code quality
  • testability
  • documentation
  • communication skills

Nice to have

  • MLOps practices
  • feature store development

What the JD emphasized

  • ML-serving infrastructure
  • ML-powered ranking models
  • productionize ML models
  • model serving optimization
  • large-scale distributed systems
  • high-throughput, low-latency backend services
  • high-scale data and feature pipelines

Other signals

  • ML model serving infrastructure
  • ML-powered ranking models
  • productionize ML models
  • MLOps practices
  • model serving optimization