Job Details

The Generative AI Innovation Center at AWS empowers customers to harness state of the art AI technologies for transformative business opportunities. Our multidisciplinary team collaborates with customers across industries to fine-tune and deploy customized generative AI applications at scale. We work closely with foundational model providers to optimize AI models for Amazon Silicon, enhancing performance and efficiency.

• Drive the development of custom Large Language Models (LLMs) across languages, domains, and modalities.
• Fine-tune state-of-the-art LLMs for diverse use cases while optimizing models for high-performance deployment on AWS's custom AI accelerators.
• Design and implement distributed training pipelines for LLMs using tools such as Fully Sharded Data Parallel (FSDP) and DeepSpeed, ensuring scalability and efficiency.
• Adapt LLMs for new languages, domains, and vision applications through continued pre-training, fine-tuning, and Reinforcement Learning with Human Feedback (RLHF).
• Optimize AI models for deployment on AWS Inferentia and Trainium, leveraging the AWS Neuron SDK and developing custom kernels for enhanced performance.
• Interact with enterprise customers and foundational model providers to understand business and technical challenges, co-developing tailored generative AI solutions.

• 3+ years of non-internship professional software development experience
• 2+ years of non-internship design or architecture experience (design patterns, reliability and scaling) of new and existing systems
• Experience programming with at least one software programming language
• Hands-on experience with deep learning and/or machine learning methods (training, fine-tuning, and inference)
• Hands-on experience with generative AI technology

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

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