Senior Applied Scientist, Artificial General Intelligence

Amazon • Sunnyvale, CA

5d

About The Position

The Artificial General Intelligence (AGI) team is looking for a Senior Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for Amazon, working with other acclaimed engineers and scientists. Key job responsibilities Join us to work as an integral part of a team that has diverse experience with GenAI models in this space. We work on these areas: - Scaling laws - Hardware-informed efficient model architecture, low-precision training - Optimization methods, learning objectives, curriculum design - Deep learning theories on efficient hyperparameter search and self-supervised learning - Learning objectives and reinforcement learning methods - Distributed training methods and solutions - AI-assisted research About the team The AGI team has a mission to push the envelope in Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.

Requirements

• PhD, or Master's degree and 5+ years of applied research experience
• 3+ years of building machine learning models for business application experience
• Experience with neural deep learning methods and machine learning
• Experience programming in Java, C++, Python or related language

Nice To Haves

• PhD in Computer Science, Electrical Engineering, Mathematics or related field
• Relevant Generative Artificial Intelligence (GenAI) research experience with LLMs and multi-modalities
• Experience with patents or publications at top-tier peer-reviewed conferences or journals
• For system researchers, familiarity with deep learning compilers, auto-parallelization, and XLA/MLIR ecosystems

Responsibilities

• Work as an integral part of a team that has diverse experience with GenAI models
• Work on scaling laws
• Work on hardware-informed efficient model architecture, low-precision training
• Work on optimization methods, learning objectives, curriculum design
• Work on deep learning theories on efficient hyperparameter search and self-supervised learning
• Work on learning objectives and reinforcement learning methods
• Work on distributed training methods and solutions
• Work on AI-assisted research

Benefits

• 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
• Number of Employees: 5,001-10,000 employees