Applied Scientist, Amazon Redshift

Amazon Amazon · Big Tech · Redmond, WA · Applied Science

Research scientist to build deep learning models for predicting query resource consumption in Amazon Redshift, covering the full ML lifecycle from data analysis to production deployment and publication.

What you'd actually do

  1. research and develop deep learning models that power resource prediction for one of the world's largest cloud data warehouses.
  2. take ownership of the end-to-end ML lifecycle, from problem formulation and data analysis to model training, evaluation, and production deployment.
  3. design novel approaches to understand queries and predict resource needs across diverse and evolving workloads.
  4. run experiments at scale on real production data, and collaborate closely with systems engineers to deliver low-latency inference in a highly available environment.
  5. publish your research at top-tier academic venues and contribute to the broader ML-for-systems community.

Skills

Required

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience programming in Java, C++, Python or related language
  • Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design

Nice to have

  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
  • Experience in solving business problems through machine learning, data mining and statistical algorithms
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • deep learning models
  • predict query resource consumption
  • intelligent workload management
  • end-to-end ML lifecycle
  • model training, evaluation, and production deployment
  • publish your research at top-tier academic venues

Other signals

  • deep learning models
  • predict query resource consumption
  • intelligent workload management
  • end-to-end ML lifecycle
  • model training, evaluation, and production deployment
  • publish research at top-tier academic venues