Connectionism: Research Blog by Thinking Machines Lab. Backed by a16z, Accel and GV.
About the role
Thinking Machines builds multimodal-first. We’re looking for new team members to advance the science of visual perception and multimodal learning. We think about how vision and language interact at scale. We design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world.
What they're looking for
- Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor
- Understanding of machine learning fundamentals, large-scale training, and distributed compute environments
- Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding
- Clarity in communication, an ability to explain complex technical concepts in writing
- Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:
More about this role
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.
Thinking Machines builds multimodal-first. We’re looking for new team members to advance the science of visual perception and multimodal learning. We think about how vision and language interact at scale. We design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world. Our goal is to create multimodal systems that support seamless integration into real-world environments.
You’ll work at the intersection of visual understanding, multimodal reasoning, and large-scale model training. You’ll help develop the architectures, data, and evaluation tools that teach AI to see, understand, and collaborate. The best candidate is curious about multimodal interfaces, has experience running large scale...
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