Software Engineer Ii, Search Science Data Infra

Amazon Amazon · Big Tech · Palo Alto, CA · Software Development

The Software Engineer II, Search Science Data Infra role at Amazon focuses on building and managing services and infrastructure for ML model training data and feature stores. This platform supports hundreds of science teams across Amazon for ML model training, feature engineering, and data quality monitoring, enabling large-scale ML operations and powering search features like query understanding, relevance ranking, and personalization. The role involves developing scalable data-intensive infrastructure and working with AWS AI services and inference services.

What you'd actually do

  1. Lead development of services and infrastructure at the intersection of machine learning, big data, and distributed systems.
  2. Our products and services empower hundreds of science teams across Amazon to deliver machine learning at scale for ML model training, Feature engineering and Data quality monitoring.
  3. You will be at the center stage for managing machine learning lifecycle and operations using AWS AI services, DL compute resources, and our core search backend services for query understanding, semantic matching, and relevance ranking.
  4. You will drive to provide a world class platform for Amazon Search engineers to comprehensively observe and introspect their applications and services both pre and post deployment to our large scale inference services.
  5. You will build scalable data-intensive infrastructure that processes huge amounts of logs, catalogs, transactional data, and telemetry signals.

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience programming with at least one software programming language

Nice to have

  • Bachelor's degree or equivalent in Computer Science
  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience

What the JD emphasized

  • ML model training data
  • Feature Store infrastructure
  • ML lifecycle and operations
  • AWS AI services
  • large scale inference services
  • data-intensive infrastructure

Other signals

  • ML model training data
  • Feature Store infrastructure
  • ML lifecycle and operations
  • AWS AI services
  • DL compute resources
  • large scale inference services
  • data-intensive infrastructure