Senior Applied Scientist, Generative AI Innovation Center

Senior Applied Scientist, Generative AI Innovation Center

Job  | Amazon Web Services Singapore Private Limited

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI.

Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions  GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently.

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.

• 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

Diverse Experiences: AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture:
Here at AWS, it’s in our nature to learn and be curious. Our employee‑led affinity groups foster a culture of inclusion that empower us to be proud of our differences.

• PhD degree in computer science, engineering, mathematics, operations research, or a highly quantitative field plus 5 years of relevant experience, or Master’s degree plus 10 years of relevant work experience
• 5+ years of hands‑on experience with Python to build, train, and evaluate models
• 5+ 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
• 2+ years demonstrated experience with Large Language Model (LLM) and Foundational Model post‑training, continual pre‑training, fine‑tuning, or reinforcement learning techniques
• Scientific publication track record at top‑tier AI/ML/NLP conferences or journals

• Demonstrated experience with building LLM‑powered agentic workflow, orchestration, and agent customization
• Experience with model optimization techniques (quantization, distillation, compression, inference optimization, etc.)
• Experience with open‑source frameworks for model…