Machine Learning Engineer, Opensearch, Opensearch, Shanghai

Amazon Amazon · Big Tech · 31, China +1 · Software Development

Machine Learning Engineer role focused on building and scaling the OpenSearch platform, a cloud-scale log analytics and search service. The role involves designing, developing, and operating software for indexing, searching, and analyzing large volumes of data, with a focus on ML frameworks, LLM fundamentals, and optimization techniques for search and analytics.

What you'd actually do

  1. Design, develop and support a world-class search platform serving individuals and businesses of all sizes
  2. Work on challenging problems in areas such as data storage, query optimization, JVM performance optimization, security, machine learning and more.
  3. Drive individual research topics and ship the contribution to OpenSearch end-to-end.
  4. Design and implement high available and high scalable applications for OpenSearch
  5. Lead team's OE/EE practice on both open source projects and manage service

Skills

Required

  • 3+ years of programming using a modern programming language such as Java, C++, or C#, including object-oriented design experience
  • Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
  • Master's degree or equivalent
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

Nice to have

  • Ph.D. in computer science, computer engineering, or related field, or experience working with or evaluating AI systems
  • Research experience in search or recommendation relevance models.
  • Research experience in search relevance evaluation and optimization.

What the JD emphasized

  • Machine Learning
  • LLM fundamentals
  • transformer architecture
  • training/inference lifecycles
  • optimization techniques
  • search relevance models
  • search relevance evaluation and optimization

Other signals

  • Machine Learning
  • LLM
  • transformer architecture
  • training/inference lifecycles
  • optimization techniques
  • search
  • log analytics
  • distributed systems