Machine Learning Engineer, Ad Response Prediction

Amazon Amazon · Big Tech · NY +1 · Software Development

Machine Learning Engineer role focused on building and optimizing ML pipelines and online serving systems for ad response prediction within Amazon Ads. The role involves driving technical direction, implementing end-to-end solutions, and working with applied scientists to improve model performance and infrastructure, with a focus on large-scale systems for recommendation, ads ranking, personalization, or search.

What you'd actually do

  1. Drive the technical direction of ML offerings across the Sponsored Products organization
  2. Design, code, troubleshoot, and support scalable ML pipelines and online serving systems
  3. Work closely with applied scientists to optimize ML model performance and infrastructure
  4. Implement end-to-end solutions—what you create is what you own

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • 2+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
  • Knowledge of machine learning model architecture and inference

Nice to have

  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
  • Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
  • 1+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field

What the JD emphasized

  • scalable ML pipelines
  • online serving systems
  • ML model performance
  • large-scale machine-learning infrastructure
  • online recommendation, ads ranking, personalization or search experience

Other signals

  • ML pipelines
  • online serving systems
  • ML model performance
  • large-scale machine-learning infrastructure
  • recommendation, ads ranking, personalization or search experience