layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

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

Research Lead, Tinker, Fine-tuning Science at Thinking Machines Lab | LAYIQ

In this role, you'll advance the science of fine-tuning and frontier post-training techniques. You’ll:
Set the research agenda: choose the problems, place the bets, and own the roadmap for pushing Tinker quality, efficiency, and reliability to the frontier.
Lead and grow the team: hire, mentor, and develop researchers, and set the bar for experimental rigor and research taste.
Stay hands-on in areas like LoRA and parameter-efficient fine-tuning and how they interact with RL and post-training.
Improve the stability, efficiency, and reliability of large-scale fine-tuning and RL runs on Tinker.
Represent the work externally through papers, technical blog posts, and community contributions.
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.
A track record of leading research – setting direction for a team or a major research effort and delivering on it.
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 and to build alignment across science, systems/infra, product.
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 Lead, Tinker, Fine-tuning Science

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