Machine Learning Engineer, Shopping Conversation Foundation

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language
- Hands-on experience with deep learning and/or machine learning methods (e.g. for training, fine tuning, and inference)
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- 1+ years of experience hands-on experience with developing, deploying, or optimizing machine learning models using a recognized ML library or framework.

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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually
Do you want to build new software tools and systems that are powered by generative AI? Do you want to work alongside a team of passionate, talented engineers and scientists on the next generation of intelligent customer-facing shopping experiences leveraging state-of-the-art deep learning and generative models? Our team is building internet-scale data solutions to power critical customer-facing shopping conversation experiences on the Amazon app and web. We are looking for a passionate, talented, and inventive engineer with a strong machine learning background, to lead the development of industry-leading technology with Large Language Models and Natural Language Processing.

The Shopping Convo Foundations org is building core foundational capabilities to train and optimize large language models. We create tools and infrastructure to measure, evaluate, and enhance high-quality language model experiences like Rufus. Our efforts aim to improve the performance and capabilities of these advanced language models. If you are thrilled about creating customer experiences that will be used by millions of people worldwide and are eager to tackle technical challenges that have never been addressed before, then this is the perfect opportunity for you.

We embrace a collaborative and inclusive culture where diverse perspectives are valued, and creativity thrives. We foster a growth mindset, continuously learning and pushing the boundaries of what's possible in the field of machine learning, generative AI and natural language processing.

As a Machine Learning Engineer in this role, you will

* Develop and maintain key services needed for evaluating and deploying large language models required for building conversational agents.
* Work with peers to investigate design approaches, prototype new technology and evaluate technical feasibility.
* Work closely with Applied scientists to process massive data, scale machine learning models while defining and optimizing criteria critical to the success of the customer experience
* Lead and influence the overall tech strategy by helping define data, enrichment, model optimizations and evaluation.
* Lead the system architecture, and spearhead the best practices that enable a quality infrastructure.
* Work in an Agile/Scrum environment to deliver high quality software.
* Tackle challenging, novel situations every day and have the opportunity to work with multiple technical teams at Amazon.
* Learn technologies and algorithms in the field of Generative AI advancing our journey to build the best conversational shopping agent.



Key job responsibilities
- Large-Scale Training Pipelines: Design and implement distributed training pipelines for LLMs using tools such as Fully Sharded Data Parallel (FSDP) and DeepSpeed, ensuring scalability and efficiency
- LLM Customization & Fine-Tuning: Adapt LLMs for new languages, domains, and vision applications through continued pre-training, fine-tuning, and Reinforcement Learning with Human Feedback (RLHF)
- Model Optimization on AWS Silicon: Optimize AI models for deployment on AWS Inferentia and Trainium, leveraging the AWS Neuron SDK for enhanced performance
- Customer Collaboration: Interact with applied scientists and foundational model providers to understand their business and technical challenges, co-developing tailored generative AI solutions