Work at the intersection of large language models, retrieval systems, and high-performance computing to advance the next generation of Artificial General Intelligence (AGI) at Amazon. Our mission is to develop highly capable AI systems by leveraging machine learning, scalable infrastructure, and responsible AI principles. Our team consists of world-class engineers, researchers, and product visionaries dedicated to building transformative solutions that will redefine industries.

We are seeking a Software Development Engineer to design and optimize large-scale, multi-modal AI systems that integrate deep learning, information retrieval, and distributed computing. This role offers an opportunity to work on search and retrieval mechanisms, model optimization, and high-performance AI inference, enhancing the reasoning and adaptability of intelligent systems. You will develop the core components that power AGI to efficiently access, process, and synthesize information at scale, pushing the boundaries of AI-driven decision-making.

You will play a key role in shaping the design, architecture, and implementation of AI-powered platforms that impact millions of users. We are looking for engineers who are passionate about working with advanced AI models, have a strong understanding of deep learning fundamentals, and can build scalable, high-performance systems. A successful candidate will have experience developing AI-driven applications, optimizing model inference, and working with large-scale data pipelines.

Key job responsibilities
- Design and implement scalable, high-throughput data processing pipelines for training and optimizing AI models.
- Develop and refine web-scale search and ranking algorithms to enhance information retrieval and knowledge synthesis for AI-driven applications.
- Architect efficient indexing and retrieval techniques to support intelligent reasoning and decision-making at scale.
- Improve the efficiency, scalability, and reliability of query processing and response generation.
- Work with deep neural networks and transformer architectures to optimize performance and reduce latency.
- Collaborate with research and ML teams to integrate state-of-the-art models into production environments.
- Build and maintain distributed systems that support large-scale AI workloads while ensuring robustness and scalability.
- Implement real-time model adaptation and continuous learning mechanisms to enhance AI capabilities.