Applied Scientist

Who you are

• 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
• PhD, or a Master's degree and experience in CS, CE, ML or related field
• Experience in building models for business application
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
• Experience using Unix/Linux
• Experience in professional software development

What the job involves

• We are looking for a Applied Scientist who is passionate about building services and tools for developers that leverage generative artificial intelligence and machine learning
• You will be part of a team building Large Language Model (LLM)-based services with the focus on enhancing the developer experience in the Cloud
• The team works in close collaboration with other AWS services such the AWS IDE Toolkit, AWS Bedrock, AWS MCPs and Agent Toolkit and develops aws-bench Open Source agent benchmark
• If you are excited about working in cloud computing and building new AWS services, then we'd love to talk to you
• As a Applied Scientist, you love designing, implementing, and evaluating machine learning solutions to difficult applied problems, and diving into complex data sources crawled from the web or internal logging tools
• You are recognized for your expertise and work closely with software engineers to drive the delivery of end-to-end modeling solutions
• Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined
• The problems that you take on require scientific breakthroughs
• You actively participate in mindful discussions with customers, engineers, and scientist peers, and define the right modeling and component design approach and implement ML solutions to achieve the desired customer experience and scientific and business outcomes
• Use Python and various open source libraries to develop and/or improve LLMs, AI agents and agentic tools for business applications
• Build benchmarks that measure AI agents performance on AWS workloads and idenitifes improvement areas and build documentation, instructions or tools to improve how agents interact with AWS
• Own the technical quality of a product where modeling improvements can improve technical designs and drive value for other scientists and engineers
• Understand the challenges that developers face when building cloud-based solutions, and develop generalizable solutions
• Collaborate with developers and pave the way towards bringing your solution into production systems. Participate in cross team projects and ensure technical blockers are resolved
• Communicate and document your research by writing internal documents, giving talks/demos, and striving to publish papers in external scientific venues