Overview

The Generative AI Innovation Center at AWS empowers customers to harness state of the art AI technologies for transformative business opportunities. Our multidisciplinary team of strategists, scientists, engineers, and architects collaborates with customers across industries to fine-tune and deploy customized generative AI applications at scale. Additionally, we work closely with foundational model providers to optimize AI models for Amazon Silicon, enhancing performance and efficiency. As an MLE on our team, you will drive the development of custom Large Language Models (LLMs) across languages, domains, and modalities. You will be responsible for fine-tuning state-of-the-art LLMs for diverse use cases while optimizing models for high-performance deployment on AWS’s custom AI accelerators. This role offers an opportunity to innovate at the forefront of AI, tackling end-to-end LLM training pipelines at massive scale and delivering next-generation AI solutions for top AWS clients.

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 and developing custom kernels for enhanced performance
• Customer Collaboration: Interact with enterprise customers and foundational model providers to understand their business and technical challenges, co-developing tailored generative AI solutions

BASIC QUALIFICATIONS

• Experience (non-internship) in professional software development
• Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design
• 3+ years of non-internship professional software development experience, design or architecture (design patterns, reliability and scaling) of new and existing systems, and 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) and with generative AI technology

PREFERRED QUALIFICATIONS

• Bachelor's degree in computer science or equivalent
• Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
• 2+ years of full software development life cycle experience including coding standards, code reviews, source control management, build processes, testing, and operations
• 1+ years of hands-on experience with developing, deploying, or optimizing machine learning models using a recognized ML library or framework

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