Liquid AI, Tokyo
As an ML Scientist focused on post-training, you will improve the capabilities and behavior of Liquid Foundation Models for Japan and the global market. You will own the full experimental loop: identifying model weaknesses, developing data and training strategies, running controlled experiments, evaluating results, and turning successful ideas into high-quality checkpoints and reusable methods.
You will work closely with Liquid's model, research, and infrastructure teams in Japan and at our US headquarters. Signals from real deployments will give you concrete, difficult problems to solve; your work will translate those signals into broader improvements in model quality, reliability, and usefulness.
This is a role for a scientist who builds. Strong ideas matter, but so do careful data work, reliable implementations, and measurable improvements in working models.
We care about demonstrated ability, scientific judgment, and the quality of your work. Degrees and publications are not required, and we encourage you to apply even if your experience does not match every item above.
Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
This is a primarily remote role for candidates already based in Japan. We typically meet in our Tokyo office once or twice a week. Working hours are fully flexible, with an expectation that you can collaborate effectively with customers in Japan and colleagues in other time zones.
The role includes travel to the United States and to relevant conferences, as well as occasional travel within Japan.
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Source: the Liquid AI Ashby job board. We last checked this job 4 h ago. We first saw it 9 days ago. A job on a job board does not always mean that the team interviews people now. We found this job board in a public list. We did not yet compare it with the careers page of the company.
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