Machine Learning Engineer II, Amazon Music - AI and Personalization

Amazon • Seattle, WA

18d

About The Position

Amazon Music is an immersive audio entertainment service that deepens
connections between fans, artists, and creators. From personalized music
playlists to exclusive podcasts, concert livestreams to artist merch, Amazon
Music is innovating at some of the most exciting intersections of music and
culture. We offer experiences that serve all listeners with our different tiers
of service: Prime members get access to all the music in shuffle mode, and top
ad-free podcasts, included with their membership; customers can upgrade to
Amazon Music Unlimited for unlimited, on-demand access to 100 million songs,
including millions in HD, Ultra HD, and spatial audio; and anyone can listen for
free by downloading the Amazon Music app or via Alexa-enabled devices. Join us
for the opportunity to influence how Amazon Music engages fans, artists, and
creators on a global scale. Learn more at https://www.amazon.com/music

We are seeking a Machine Learning Engineer to join the Amazon Music AI and
Personalization team and drive model training efficiency and inference
optimization improvements. In this role, you will work at the intersection of
machine learning and systems engineering, ensuring our models train faster, cost
less, and run efficiently in production environments. You will collaborate
closely with research scientists, platform engineers, and product teams to
deliver scalable, high-performance ML solutions that help customers discover
great new products and save money on products that they are evaluating.

Requirements

• Bachelor's degree in computer science or equivalent
• 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Experience in machine learning, data mining, information retrieval, statistics or natural language processing
• Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
• Experience building complex software systems that have been successfully delivered to customers
• Experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and PyTorch
• Experience in production, monitoring and metrics reporting
• Experience building, deploying, and maintaining large-scale machine learning infrastructure using distributed data processing frameworks such as Spark or Ray
• Experience owning and operating production services, including on-call responsibilities, incident management, and operational metrics

Nice To Haves

• Master's degree in computer science or equivalent
• Expertise in large-model inference optimization, including techniques such as quantization, pruning, and distillation
• Demonstrated experience designing semantic search or RAG pipelines, integrating embeddings, vector stores, and generative models
• Proficiency in online and offline experimentation, evaluation frameworks, and metrics instrumentation for ML systems
• Experience with service-oriented architectures, microservices design patterns, and managing service dependencies in complex ML systems
• Strong collaboration and communication skills, with the ability to bridge science and engineering to deliver end-to-end ML solutions

Responsibilities

• Design and implement strategies to improve training throughput and reduce time-to-convergence
• Profile and eliminate bottlenecks in data loading, preprocessing, and model computation
• Develop and maintain training infrastructure that scales efficiently with model and dataset size
• Optimize models for low-latency, high-throughput production inference
• Implement and benchmark inference optimizations across various hardware targets (GPU, CPU, edge devices)
• Establish performance benchmarks and monitoring for inference pipelines
• Own production services that support ML decision models, including ranking services, orchestration layers, and model-serving infrastructure
• Participate in on-call rotation to ensure service reliability, respond to operational issues, and drive continuous improvement
• Design and implement monitoring, alerting, and observability solutions for ML services to proactively identify and resolve issues
• Manage service dependencies, API contracts, and integration points between ML models and downstream systems
• Drive operational excellence through automation, runbook development, and post-incident reviews
• Partner with research teams to understand model architectures and identify optimization opportunities
• Collaborate with Science/ML teams on service integration points and ownership boundaries for ML components
• Contribute to best practices and tooling for ML efficiency across the organization
• Evaluate emerging hardware and software technologies for potential adoption

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level