About the Role
The role of post-training researchers sits at the core of our roadmap. This is the critical bridge between raw model intelligence and a system that is actually useful, safe, and collaborative for humans.
This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.
What You’ll Do
- Develop and tune the recipe: iterate on post-training recipes, consisting of a collection of datasets, training stages, and hyperparameters. Measure how recipe choices affect various metrics.
- Iterate on evals: post-training involves a never-ending loop of defining a set of evaluations, optimizing them, and then realizing your existing evals don’t capture what matters. You’ll be responsible for both making numbers go up, and making sure the numbers are meaningful.
- Debug and understand: while tuning the details of a training configuration, we often observe results that don’t quite make sense. You’ll be responsible for both getting things to work, and developing a deeper understanding, which we can bring to the next problem.
- Scale and explore: post-training will involve a combination of scaling the existing methodologies and developing new ones. We’ll want to both measure how performance metrics scale with dataset size, and explore using a completely different kind of training dataset.
- Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.
Skills and Qualifications
Minimum qualifications:
- Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
- Clarity in communication, an ability to explain complex technical concepts in writing.
Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:
- 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.
- PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.