Thinking Machines Lab, San Francisco
Tinker https://thinkingmachines.ai/tinker/ is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs to open access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training models with their own data, algorithms, and for their own needs.
This role is all about building our training systems for Tinker, including RL systems, numerics, kernels, and beyond.
In this role, you'll develop frontier customization techniques and help build the best post-training engine in the industry, drawing on a whole-stack understanding recipes, data pipelines, and training systems (numerics, kernels, and beyond).
You'll engage directly with the researchers and companies pushing Tinker to its limits. This role is working with both our internal research teams as well as contributing to open science and external partners.
You’ll co-design RL algorithms and training systems across the whole stack, from RL science down to numerics and kernels, to enable anyone to post-train frontier models. You’ll debug RL runs in the wild, optimize post-training pipelines, and help users reach frontier-level results, which in turn makes our platform and models the best they can be.
Preferred qualifications – we encourage you to apply if you meet some but not all of these:
The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
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