Connectionism: Research Blog by Thinking Machines Lab. Backed by a16z, Accel and GV.
About the role
At Thinking Machines we build tools that enable people to make AI their own, customizing models to serve their unique needs. This includes the ability to train model weights.
What they're looking for
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales
- Clarity in communication, an ability to explain complex technical concepts in writing
- Strong interest in our mission to enable custom models
- Preferred qualifications — we encourage you to apply if you meet some but not all of these:
- A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs
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.
At Thinking Machines we build tools that enable people to make AI their own, customizing models to serve their unique needs. This includes the ability to train model weights.
In this role, you'll work on frontier customization techniques and help build the best post-training engine in the industry – Tinker – drawing on a whole-stack understanding of RL science. Findings directly shape Tinker's training defaults, API design, and the open-source Tinker Cookbook. You'll work with our internal research teams as well as contributing to open science for external partners.
In this role, you'll advance the science of fine-tuning and frontier post-training techniques. You’ll:
Contribute to areas like LoRA and parameter efficient fine-tuning and how to push customization quality, efficiency, and reliability to the frontier.
Ship research into product:...
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