Applied Scientist II - AMZ9674020

Amazon•Mountain View, CA

36d

About The Position

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.

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.
Employer will accept 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 as equivalent to the Master's degree and one year of research or work experience.
Must have one year of research or work experience in the following skill(s): (1) programming in Java, C++, Python, or equivalent programming language; and (2) 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.