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About Openai (Wikipedia Record):
OpenAI is an American artificial intelligence (AI) public benefit corporation (PBC) headquartered in San Francisco, California. It develops proprietary generative AI models, particularly its generative pre-trained transformer (GPT) series of large language models. Its release of ChatGPT in November 2022 has been credited with catalyzing the AI boom; as of September 2026, ChatGPT is the fifth-most-visited website globally. OpenAI also releases the GPT Image models and Codex, an AI coding agent. In March 2026, OpenAI closed a funding round with a post-money valuation of US$852 billion, making it one of the most valuable AI pure play companies in the world, rivalled only by Anthropic.
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Openai
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About the Role
ABOUT THE TEAM
OpenAI’s API Multicloud team is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS https://openai.com/index/openai-on-aws/. The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in AWS-native environments, in close partnership with Amazon and internal teams across Codex, Research, Safety Systems, and Applied.
The team is focused on bringing core developer and enterprise capabilities into cloud-native environments, including AWS-hosted Codex, model customization / post-training as a service, and new stateful runtime environments for agentic workloads. This work sits at the intersection of production ML systems, developer platforms, model behavior, and large-scale infrastructure.
ABOUT THE ROLE
We’re hiring Machine Learning Engineers to build and improve the AI systems that help strategic partners adapt OpenAI models to important use cases in cloud-native environments. This role spans post-training workflows, evaluation, data pipelines, model behavior, and API/infrastructure integration.
You’ll work at the boundary between partner needs and core ML systems: helping teams understand what is and isn’t working, diagnosing issues in training and evaluation workflows, and turning those learnings into improvements to the underlying platform. You should enjoy working with external technical partners, extracting the real goal from messy requests, and pushing back or reframing when the requested experiment is not the highest-leverage path. You’ll collaborate closely with Research, Applied, Safety Systems, infrastructure teams, and external technical partners to solve ambiguous model-performance problems. When you succeed, strategic partners and internal teams will be able to improve model behavior with confidence, driving measurable product improvements while the systems behind that work become more reliable, scalable, and effective over time.
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