Research, Tinker, RL Systems

Highlights

• Build the best post-training engine to power Tinker's fine-tuning API.
• Co-design RL algorithms and training systems across the full stack.
• Help researchers push Tinker to its limits for frontier-level results.

What they offer

• Health, dental, and vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support as needed

Ideal candidate

A systems-minded researcher who thrives debugging distributed training and turning RL science into reliable platforms. Excited by open science and empowering others to customize frontier models.

Requirements

• Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or related discipline
• 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 and ability to explain complex technical concepts in writing
• Strong interest in working on Tinker and increasing usefulness and adoption
• Strong grasp of probability, statistics, and ML fundamentals
• Experience with RL training stability techniques for large runs
• Familiarity with low-precision training and inference, numerics and quantization
• Hands-on work with LLM serving stacks (e.g., SGLang, vLLM, TokenSpeed, or custom engines)
• Experience with scaling studies for large models
• Contributions to open-source training or inference frameworks