Amazon
Company
Member of Technical Staff, Data Platform, AGI
US, CA, San Francisco
Job Description
Amazon AGI Autonomy develops foundational capabilities for useful AI agents. We are the research lab behind Amazon Nova Act, a state-of-the-art computer-use agent. Our work combines Large Language Models (LLMs) with Reinforcement Learning (RL) to solve reasoning, planning, and world modeling in the virtual world. We are a small, talent-dense team with the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. Come be a part of our journey!
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About the team
We’re looking for a generalist software engineer to build and evolve our internal data platform. The team builds data-intensive services that ingest, process, store, and distribute multi-modal training data across multiple internal and external sources. This work emphasizes data integrity, reliability, and extensibility in support of large-scale training and experimentation workloads. The team also builds and maintains APIs and SDKs that enable product engineers and researchers to build on top of the platform.
As research directions change, so does our data, and today the team is focused on hardening the platform to reliably deliver an evolving set of data schemas, sources, and modalities. By building strong foundations and durable abstractions, we aim to enable new kinds of tooling and workflows over time. The team will play a key role in shaping them as the research evolves.
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Key job responsibilities
* Build and operate reliable, performant backend and data platform services that support continuous ingestion and use of multi-modal training data.
* Identify and implement opportunities to accelerate data generation, validation, and usage across training and evaluation workflows from multiple internal and external sources.
* Partner closely with Human Feedback, Data Generation, Product Engineering, and Research teams to evolve and scale the data platform, APIs, and SDKs.
* Own projects end to end, from technical design and implementation through deployment, observability, and long-term maintainability.
* Write clear technical documentation and communicate design decisions and tradeoffs to stakeholders across multiple teams.
* Raise the team’s technical aptitude through thoughtful code reviews, knowledge sharing, and mentorship.
---
About the team
We’re looking for a generalist software engineer to build and evolve our internal data platform. The team builds data-intensive services that ingest, process, store, and distribute multi-modal training data across multiple internal and external sources. This work emphasizes data integrity, reliability, and extensibility in support of large-scale training and experimentation workloads. The team also builds and maintains APIs and SDKs that enable product engineers and researchers to build on top of the platform.
As research directions change, so does our data, and today the team is focused on hardening the platform to reliably deliver an evolving set of data schemas, sources, and modalities. By building strong foundations and durable abstractions, we aim to enable new kinds of tooling and workflows over time. The team will play a key role in shaping them as the research evolves.
---
Key job responsibilities
* Build and operate reliable, performant backend and data platform services that support continuous ingestion and use of multi-modal training data.
* Identify and implement opportunities to accelerate data generation, validation, and usage across training and evaluation workflows from multiple internal and external sources.
* Partner closely with Human Feedback, Data Generation, Product Engineering, and Research teams to evolve and scale the data platform, APIs, and SDKs.
* Own projects end to end, from technical design and implementation through deployment, observability, and long-term maintainability.
* Write clear technical documentation and communicate design decisions and tradeoffs to stakeholders across multiple teams.
* Raise the team’s technical aptitude through thoughtful code reviews, knowledge sharing, and mentorship.
Amazon
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