ai&, Yokohama +1
ai& is a new global AI technology company dedicated to meeting the world's growing demand for AI. Our vision is twofold: to serve as a premier AI lab specializing in localization, and to act as a global infrastructure and compute provider. We are building a unified, optimized global platform that integrates next-generation data centers and infrastructure, heterogeneous compute serving, and advanced model services. We believe that the most effective way to build and scale AI is to own the stack from top to bottom.
At ai&, we empower small teams with the autonomy needed to tackle significant challenges. Our approach is to deconstruct large problems into manageable components and solve complex issues collaboratively. We seek highly motivated, mission-driven individuals who demonstrate strong personal agency. We value curiosity as the foundation of talent, and we are looking for people eager to develop alongside our evolving technology and expanding business.
We are actively hiring worldwide, with presence in Tokyo, SF, Austin, and Toronto. We are more than happy to meet exceptional talent where they are.
Role overview This is both a research and an engineering role. You will own post-training end to end for ai&'s internal custom models and for enterprise customers who need models adapted to their domains. That means you are running experiments, building the pipelines those experiments depend on, and delivering results that ship.
You will work directly with enterprise customers to translate their requirements into post-training workflows, own the delivery, and feed every applied learning back into ai&'s core stack. At the same time you are an active contributor to our RL practice — designing training environments, integrating techniques from the research frontier, and building the continual learning infrastructure that keeps our models improving over time.
We want someone who thinks end to end across data, training, alignment, and evaluation as a single system, and who is pragmatic enough to optimize for model quality and real outcomes above all else.
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