Amazon has reorganized multiple advanced engineering teams into a single group that now manages artificial intelligence models, chip design, and quantum computing research.
This updated structure includes the team responsible for the Nova family of foundation models, which power a growing number of services used by both internal teams and external cloud customers. It also includes the Annapurna Labs group, which develops custom processors such as Graviton and Trainium that support many of Amazon’s compute and machine learning workloads.
Previously, these teams were spread across separate parts of Amazon’s corporate structure. AI model development was handled largely within the AGI group, operating alongside the Alexa voice assistant team. Chip engineering was centered within AWS infrastructure, while quantum computing was managed as a distinct research effort.
These units now report under a common leadership framework, with shared goals and aligned timelines. The new setup connects the development of machine learning models directly to the underlying infrastructure.
Why It Matters: The teams now working together build and maintain systems used throughout Amazon’s technology stack. With this new structure, they follow a single development process that connects research and deployment more directly than before.
- Foundation Model Development Within Infrastructure Engineering: The Nova models power applications for customer service, developer tooling, and enterprise tasks. They rely on large-scale compute systems and frequent updates. By moving model development into the same group that manages the underlying hardware, Amazon has reduced the steps needed to test and release changes. Engineers responsible for model behavior now work directly with those running the environments where the models operate, removing a handoff that previously slowed releases and tuning.
- A Shared Workflow for Custom Processor and Model Development: Amazon’s in-house chips are built with specific purposes. Graviton focuses on general-purpose computing, while Trainium is designed for model training. The teams designing these processors now work closely with the teams building the models that run on them. Testing and architectural planning happen within a single workflow. This removes the need for separate alignment meetings, since technical decisions and deployment plans are handled within the same reporting structure.
- Quantum Computing Research Is Integrated Into Applied Engineering: Amazon’s quantum team works on physical hardware and algorithm development, alongside long-range research. The group is now part of the same engineering track that supports production models and compute infrastructure. Quantum engineers work alongside teams that manage deployed systems. While their research goals remain distinct, they now use shared processes and planning tools.
- Leadership Changes Accompany the Reorganization: Rohit Prasad, who led the AGI group and played a central role in Amazon’s early conversational AI efforts, will leave the company. His work included shaping Alexa’s direction and building the team behind Nova. Pieter Abbeel, who joined Amazon through the acquisition of Covariant, now leads foundation model research. His background spans robotics and learning systems, and he continues to guide applied work across the company.
- AI Engineering Is No Longer Split Across Business Units: Previously, Amazon’s AI efforts were spread across separate divisions. Alexa and AWS followed different timelines and ran separate infrastructure, even when working on related capabilities. That separation has been removed. Model training, hardware design, deployment coordination, and long-term system improvements now sit within one organization. This reduces duplicated work and allows teams to share tools and systems without extra layers of approval.
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