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Amazon Software Development Engineer ML AGI Foundations 
United States, California, Sunnyvale 
115038728

12.08.2024
DESCRIPTION

As a SDE with the AGI team, you will be a key contributor to the development of novel algorithms and modeling techniques to advance the state of the art with multimodal systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multimodal Large Language Models (LLMs) and Generative Artificial Intelligence (Gen AI) in Computer Vision. You will have significant influence on our overall strategy by helping define data, enrichment, model optimizations and evaluation. You will drive the system architecture, and spearhead the best practices that enable a quality infrastructure.Key job responsibilities
- Responsible for the development and maintenance of key platforms needed for developing multimodal models- Work closely with Applied scientists to process data, scale machine learning models while optimizing
- Will work in an Agile/Scrum environment to deliver high quality software against aggressive schedules.

BASIC QUALIFICATIONS

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language


PREFERRED QUALIFICATIONS

- Bachelor's degree in computer science or equivalent
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Experience designing, developing, and optimizing high-performance, large-scale machine learning systems.
- Proficient with PyTorch, with demonstrated experience in both training and inference of deep learning models.
- Hands-on experience with Kubernetes and training with GPU clusters.