Research Engineer - Agency and Reasoning

As a Research Engineer - Agency and Reasoning, you will be a core contributor to Zyphra's Agency and Reasoning Team. You will be involved with performing novel research in reinforcement learning, post-training, and human preference learning, and applying your ideas at scale to our next generation of language models.

Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued

We strongly value new and crazy ideas and are very willing to bet big on new ideas

We move as quickly as we can; we aim to minimize the bar to impact as low as possible

We all enjoy what we do and love discussing AI

Strong research taste and intuition

The ability to work through a research project from conception to execution to write-up

Strong implementation and prototyping skillset

A researcher who can take an idea from conception to experimentation extremely quickly

The ability to work well and cooperate with others in a high-paced research setting

Curiosity, interest, and joy in understanding intelligence.

Experience and aptitude with reinforcement learning, either in the context of language model reasoning or more classical RL tasks

Experience with language-model-supervised fine-tuning and preference-learning methods, such as DPO and simPO.

Experience with context-length extension methods

A good intuitive ability to understand model behaviors and correct them through iterative fine-tuning

Interest in grappling in detail with data and spending significant time involved in data engineering and synthetic data generation

Postgraduate degree in a scientific subject (Computer Science, EE/EECS, Mathematics, Physics)

Previously published machine learning research in well-respected venues

Highly proficient with PyTorch and Python

We are excited and able to rapidly learn new fields and implement new ideas

Excellent communication and collaboration skills, and can work effectively on both research and engineering implementation at scale