Job Description

Locations

• United States, WA, Seattle, Seattle, WA, United States
• United States, VA, Arlington, Arlington, VA, United States
• United States, WA, Bellevue, Bellevue, WA, United States
• United States, NY, New York, New York, NY, United States

Salary

Skills Required

• - PhD degree in computer science, engineering, mathematics, operations research, or in a highly quantitative field plus 5 years of relevant experience, or Master’s degree plus 10 years of relevant work experienceintermediate
• - 5+ years of hands on experience with Python to build, train, and evaluate modelsintermediate
• - 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 computingintermediate
• - 2+ years demonstrated experience with Large Language Model (LLM) and Foundational Model post-training, continual pre-training, fine-tuning, or reinforcement learning techniques.intermediate

Required Qualifications

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

Preferred Qualifications

• - Demonstrated experience with building LLM-powered agentic workflow, orchestration, and agent customization (experience)
• - Experience with model optimization techniques (quantization, distillation, compression, inference optimization etc.) (experience)
• - Experience with open-source frameworks for model customization like trl, verl, and for building LLM-powered applications like LangChain, LlamaIndex, and/ or similar tools (experience)
• - Strong communication skills, with attention to detail and ability to convey rigorous technical concepts and considerations to non-experts (experience)
• - Demonstrated ability to identify and frame technical problems from broad product-level and business-level problem areas. (experience)
• - Track record of leading the design, implementation and delivery of scientifically-complex solutions that span multiple teams. (experience)
• - Experience driving scientific agenda and technical strategy in a team, including building consensus on technical approaches and mentoring other scientists to improve their technical capabilities. (experience)
• Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. (experience)

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

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