Amazon Applied Scientist Model Customization Generative 

United States, Virginia 

433417861

You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.We’re looking for Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.
Key job responsibilities
As an Applied Scientist, you will
• Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges
• Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production
• Help customers optimize their solutions through approaches such as model selection, training or tuning, right-sizing, distillation, and hardware optimization
• Provide customer and market feedback to product and engineering teams to help define product direction

- PhD degree in computer science, engineering, mathematics, operations research, or in a highly quantitative field, or Master’s degree plus 5 years of relevant work experience
- 5+ years of hands on experience with Python to build, train, and evaluate models
- 2+ years of experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience with design, development, and optimization of generative AI solutions, algorithms, or technologies
- Scientific publication track record at top-tier AI/ML/NLP conferences or journals

- • 2+ years demonstrated experience with Large Language Model (LLM) and Foundational Model post-training, continual pre-training, fine-tuning, or reinforcement learning techniques.
- • Demonstrated experience with building LLM-powered agentic workflow, orchestration, and agent customization
- • Track record of building and deploying ML models at scale
- • Experience with model optimization techniques (quantization, distillation, compression, inference optimization etc.)
- • Experience with open-source frameworks for model customization like trl, verl, and for building LLM-powered applications like LangChain, LlamaIndex, and/ or similar tools
- • Hands-on experience building generative AI applications on AWS using services such as Amazon Bedrock and Amazon SageMaker
- • Strong communication skills, with attention to detail and ability to convey rigorous technical concepts and considerations to non-experts