Machine Learning Engineer, Ad Response Prediction

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Machine Learning Engineer at Amazon Ads focused on Sponsored Products and Brands, re-imagining advertising with generative AI. The role involves designing, coding, and supporting scalable ML pipelines and online serving systems, optimizing ML model performance and infrastructure, and implementing end-to-end solutions. It requires driving technical direction, building and growing teams, and collaborating on product direction. The team operates on a large product catalog with strict latency constraints and works with research scientists to deliver relevant ads.

What you'd actually do

  1. Drive the technical direction of our 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
  5. Own technical vision and direction — Identify problems, develop solutions, and embrace performance metrics to assess system health

Skills

Required

  • 3+ years of professional software development experience
  • 3+ years of full software development life cycle 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
  • 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
  • Experience developing, deploying and managing AI products at scale

What the JD emphasized

  • scalable ML pipelines
  • online serving systems
  • ML model performance
  • end-to-end solutions
  • large-scale machine-learning infrastructure
  • AI products at scale
  • strict latency constraints

Other signals

  • ML pipelines
  • online serving systems
  • ML model performance
  • end-to-end solutions
  • large-scale machine-learning infrastructure
  • AI products at scale