Amazon.com Services LLC

Software Development, entertainment

MachineLearningEngineerII,AmazonMusic-AIandPersonalization

$144–194k Seattle, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“Machine Learning Engineer II, Amazon Music - AI and Personalization at Amazon.com Services LLC. Skills: Machine Learning, Systems Engineering, Model Training, Inference Optimization. Design model training strategies. Implement model training strategies”

What You'll Achieve.

Improve model training efficiency; Reduce model training costs; Run models efficiently in production; Deliver scalable ML solutions; Deliver high-performance ML solutions; Help customers discover products; Help customers save money

Industry & Context.

Software Development, entertainment
Problems you'll solve

Eliminate bottlenecks; Resolve issues; Troubleshooting

Eligibility Requirements

On-call rotation

What They're Looking For.

Must Have

3+ years full SDLC, Machine learning experience, Data mining experience, Information retrieval experience, Statistics experience, Natural language processing experience, Programming with Java, Programming with C++, Programming with C#, Object-oriented design experience, ML/LLM fundamentals experience, Build complex software systems, Experience with MXNet, Experience with TensorFlow, Experience with Caffe, Experience with PyTorch, Production monitoring experience, Metrics reporting experience, Build ML infrastructure, Deploy ML infrastructure, Maintain ML infrastructure, Experience with Spark, Experience with Ray, Own production services, On-call responsibilities experience

Nice to Have

Master's degree preferred, Large-model inference optimization expertise, Quantization techniques expertise, Pruning techniques expertise, Distillation techniques expertise, Semantic search pipelines design, RAG pipelines design, Integrate embeddings experience, Vector stores integration experience, Generative models integration experience, Online experimentation proficiency, Offline experimentation proficiency, Evaluation frameworks proficiency, Metrics instrumentation proficiency, Service-oriented architectures experience, Microservices design patterns experience, Manage service dependencies experience

What You'll Do.

Design model training strategies

Implement model training strategies

Improve training throughput

Reduce time-to-convergence

Profile data loading bottlenecks

Eliminate data loading bottlenecks

Profile preprocessing bottlenecks

Eliminate preprocessing bottlenecks

Profile model computation bottlenecks

Eliminate model computation bottlenecks

Develop training infrastructure

Maintain training infrastructure

Scale training infrastructure

Optimize models for inference

Implement inference optimizations

Benchmark inference optimizations

Establish performance benchmarks

Establish inference monitoring

Own production ML services

Own orchestration layers

Own model-serving infrastructure

Participate in on-call rotation

Respond to operational issues

Drive continuous improvement

Design monitoring solutions

Implement monitoring solutions

Design alerting solutions

Implement alerting solutions

Design observability solutions

Implement observability solutions

Manage service dependencies

Manage integration points

Drive operational excellence

Conduct post-incident reviews

Partner with research teams

Understand model architectures

Identify optimization opportunities

Collaborate on service integration

Collaborate on ownership boundaries

Contribute to best practices

Contribute to ML efficiency tooling

Evaluate hardware technologies

Evaluate software technologies

How You'll Work.

Team & Collaboration

Collaborate with research scientists; Collaborate with platform engineers; Collaborate with product teams; Collaborate with Science/ML teams

Process & Methodology

Software development life cycle

Full Job Description

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. Key job responsibilities Model Training Optimization - 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 Inference Optimization - Optimize models for low-latency, high-throughput production inference - Implement and benchmark inference optimization

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