Senior Machine Learning Engineer II - LLM

Moveworks Moveworks · Enterprise · Mountain View, CA · Machine Learning

This role focuses on building and optimizing scalable machine learning infrastructure for training, evaluation, and deployment of large language models (LLMs). It involves creating abstractions for ML workflows, optimizing LLM latency, and ensuring the scalability of ML services, ultimately impacting the customer experience with AI.

What you'd actually do

  1. Design, build and optimize scalable machine learning infrastructure to support training, evaluation, and deployment of large language models.
  2. Build abstractions to automate various steps in different ML workflows
  3. Collaborate with cross functional teams of engineers, data analytics, machine learning experts, and product to build new features
  4. Leverage your experience to drive best practices in ML and data engineering

Skills

Required

  • 2+ years of industry experience in Machine Learning, Infrastructure or related fields
  • Experience with deep learning framework such as Pytorch or Huggingface or LLM serving frameworks such as vLLM or TensorRT-LLM.
  • Experience with building and scaling end-to-end machine learning systems
  • Experience building scalable micro services and ETL pipelines
  • Expertise in Python
  • experience with performant language such as C++ or GoLang
  • Bachelor's in Computer Science, Computer Engineering, Mathematics, or equivalent field.

Nice to have

  • Experience with ML Inference optimization using TensorRT.
  • Experience with distributed training frameworks such as Deepspeed.
  • Experience in managing and scaling GPU Inference services via Kubernetes

What the JD emphasized

  • critical in building, optimizing and scaling end-to-end machine learning systems
  • absolutely critical to the long term scalability of our core AI product

Other signals

  • building and productionizing ML infrastructure
  • optimizing and scaling end-to-end machine learning systems
  • LLM latency optimization
  • scalability of services
  • optimization of core algorithms