Research, Finetuning Science

Job summary

Responsibilities

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.

• 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: inform Tinker's training defaults and primitives, and codify best-practice methods as recipes in the Tinker Cookbook.
• Improve the stability, efficiency, and reliability of large-scale fine-tuning and RL runs on Tinker.
• Share what you learn through papers, technical blog posts, and community contributions.

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.
• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.

Tech stack

• Python
• PyTorch
• TensorFlow
• JAX