layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Research, Finetuning Science at Thinking Machines Lab | LAYIQ

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: 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.
You’ll contribute to areas like LoRA, parameter-efficient fine-tuning, how things interact with RL and post-training, and how to push customization quality, efficiency, and reliability to the frontier.
Skills and Qualifications
Required qualifications: 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.
Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models.
Experience managing or analyzing human data collection campaigns or large-scale annotation workflows.
Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration.
Experience with RL training stability techniques for large runs.
PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Research, Finetuning Science

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