Applied Scientist II - AMZ10519502

Amazon • Mountain View, CA

1d • $171,600 - $222,200 • Onsite

About The Position

This role involves participating in the design, development, evaluation, deployment, and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. The position requires developing and/or applying statistical modeling techniques (e.g., Bayesian models and deep neural networks), optimization methods, and other ML techniques to various business and engineering applications. The scientist will routinely build and deploy ML models on available data, research and implement novel ML and statistical approaches to add business value, and mentor junior engineers and scientists. The role will work across industries including financial services, healthcare, retail, and manufacturing, developing AI solutions tailored to each sector's requirements. Specific focus areas include generative AI, natural language processing, and large-scale model training and deployment. This includes designing custom machine learning algorithms for generative AI applications, fine-tuning foundation models using customer datasets with techniques like LoRA and parameter-efficient methods, and evaluating existing ML frameworks, extending them with custom components. The role also involves researching and applying cutting-edge ML principles, including novel training methodologies and reinforcement learning techniques, developing new algorithms for model optimization (distillation, hardware-specific optimizations), and conducting applied research on generative AI architectures, training strategies, and optimization techniques through prototyping and benchmarking. Investigation of approaches such as retrieval-augmented generation, fine-tuning methodologies, and reinforcement learning from human feedback is also expected.

Requirements

• Master's degree or foreign equivalent degree in Computer Science, Machine Learning, Statistics, or a related field and one year of research or work experience in the job offered or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation.
• Alternatively, a Bachelor's degree or foreign equivalent degree in Computer Science, Machine Learning, Statistics, or a related field and five years of progressive postbaccalaureate research or work experience in the job offered or a related occupation.
• One year of research or work experience in programming in Java, C++, Python, or equivalent programming language.
• One year of research or work experience in conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering.

Nice To Haves

• Please see job description and the position requirements above.

Responsibilities

• Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.
• Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.
• Routinely build and deploy ML models on available data.
• Research and implement novel ML and statistical approaches to add value to the business.
• Mentor junior engineers and scientists.
• Work across industries including financial services, healthcare, retail, and manufacturing, developing AI solutions tailored to each sector's requirements.
• Work on generative AI, natural language processing, and large-scale model training and deployment.
• Design custom machine learning algorithms for generative AI applications and fine-tune foundation models using customer datasets with techniques like LoRA and parameter-efficient methods.
• Evaluate existing ML frameworks and extend them with custom components to meet specific customer requirements.
• Research and apply cutting-edge ML principles including novel training methodologies and reinforcement learning techniques to create innovative solutions.
• Develop new algorithms for model optimization, including distillation and hardware-specific optimizations.
• Conduct applied research on generative AI architectures, training strategies, and optimization techniques through prototyping and benchmarking.
• Investigate approaches including retrieval-augmented generation, fine-tuning methodologies, and reinforcement learning from human feedback.
• Mentor junior engineers and scientists.

Benefits

• equity
• sign-on payments
• other forms of compensation
• medical
• financial
• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level