Senior Machine Learning Engineer, Sponsored Products and Brands Relevance

Senior Machine Learning Engineer role focused on real-time ML serving for Sponsored Products and Brands Relevance. This onsite position in Palo Alto, CA offers a salary range of USD 193,300 to 261,500 per year. You will shape technical direction, mentor engineers, and advance ad relevance using deep learning, NLP / large language models, and distributed systems at Amazon scale.

Benefits

• Sign-on payments
• Restricted stock units (RSUs)
• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• Paid time off
• Parental leave

Responsibilities

• Set and guide the technical roadmap for ML initiatives spanning deep learning, AWS infrastructure, AutoML, and real-time serving systems
• Architect, build, and own scalable offline ML pipelines and online serving components capable of billions of requests daily with millisecond latency
• Collaborate with applied scientists to optimize model performance, enhance ML productivity, and strengthen the platform that powers scientific innovation
• Troubleshoot and support high-volume, low-latency distributed systems; own the systems you build
• Mentor junior engineers to deliver high-impact products and services for Amazon customers and sellers
• Make informed technology choices that balance innovation velocity with operational excellence and business needs

Requirements

• 8+ years of non-internship professional software development experience
• 10+ years of programming with at least one software programming language
• 5+ years of leading design or architecture focused on patterns, reliability, and scaling for new and existing systems
• Experience as a mentor, tech lead, or leading an engineering team
• Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
• 5+ years building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization, or search experiences
• Proven ability to drive technical decisions across teams and deliver end-to-end from design to production deployment

Technologies

• PyTorch
• TensorFlow
• SageMaker
• Triton
• vLLM
• Spark
• AutoML
• AWS