Machine Learning Engineer, Amazon Tablets

Amazon Amazon · Big Tech · M, Spain +1 · Machine Learning Science

ML Engineer role focused on building and deploying AI/ML products for Amazon Tablets, specifically enhancing customer engagement through personalized recommendations and content ranking. The role involves end-to-end ML pipeline development, MLOps, and leveraging deep learning, LLMs, and generative AI for on-device experiences. It emphasizes shipping customer-facing ML solutions at Amazon scale.

What you'd actually do

  1. Design and develop AI/ML products that involve large-scale data processing and modeling
  2. Be responsible to help define requirements, create software designs, implement code to these specifications, and support products while deployed and used by our customers
  3. Work with applied scientists, data scientists, engineers, and product managers to design and deliver AI/ML solutions in production at scale.
  4. Develop ML workflows and end-to-end pipelines for data preparation, training, deployment, monitoring, etc., and ensure the quality of architecture and design of our ML systems and data infrastructure.
  5. Maintain and continuously improve our existing ML workflows, MLOps, and infrastructure.

Skills

Required

  • Experience (non-internship) in professional software development
  • Experience in machine learning, data mining, information retrieval, statistics or natural language processing
  • Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
  • Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
  • Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design

Nice to have

  • Bachelor's degree in computer science or equivalent
  • Experience in several of the following areas: machine learning, statistics, deep learning, natural language processing, or information retrieval
  • Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
  • Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
  • Experience in building scalable machine-learning infrastructure and big data systems.

What the JD emphasized

  • scalable AI/ML systems
  • deploying models into production to serve millions of customers
  • delivering high business impact
  • Amazon-scale
  • large-scale data processing and modeling
  • deliver AI/ML solutions in production at scale
  • end-to-end pipelines
  • ML systems and data infrastructure
  • existing ML workflows, MLOps, and infrastructure
  • scalable machine-learning infrastructure and big data systems

Other signals

  • design and development of scalable AI/ML systems
  • deploying models into production to serve millions of customers
  • building AI-driven products
  • delivering high business impact
  • customer-focused machine learning
  • Amazon-scale
  • intelligent customer experience
  • advanced deep learning
  • large language models
  • generative AI
  • machine learning on device
  • design and develop AI/ML products
  • large-scale data processing and modeling
  • create software designs
  • implement code to these specifications
  • support products while deployed
  • design and deliver AI/ML solutions in production at scale
  • Develop ML workflows and end-to-end pipelines
  • data preparation, training, deployment, monitoring
  • quality of architecture and design of our ML systems and data infrastructure
  • Maintain and continuously improve our existing ML workflows, MLOps, and infrastructure
  • Deliver customer-facing and internal ML solutions
  • build and train models that deliver better recommendations and rank content based on user behavior
  • build large scale services leveraging AWS
  • building scalable machine-learning infrastructure and big data systems