Guided search and reinforcement learning for agentic systems
Reinforcement learning for reasoning models
Web-scale problem collection for training agents
Efficient model distillation
Some examples of what your responsibilities might include are:
Conducting experiments to figure out efficient ways to train a large language model on traces of interactions with various environments
Exploring methods of guided generation and search in the trajectory space
Coming up with ways to mine relevant data at web scale and figuring out efficient ways to use this data in model post-training
Conducting experiments with different reinforcement learning configurations in verifiable domains
Exploring methods to train AI agents on tasks with non-verifiable reward signals
We expect you to have:
A profound understanding of theoretical foundations of machine learning and reinforcement learning
Deep expertise in modern deep learning for language processing and generation
Substantial experience with training large models on multiple computational nodes
Strong software engineering skills (we mostly use python)
Deep experience with modern deep learning frameworks (we use jax)
Strong communication and leadership abilities
Experience designing, executing, and analyzing machine learning experiments with proper statistical rigor
Ability to formulate research questions, design experiments to test hypotheses, and draw meaningful conclusions from results
Ability to document research findings clearly and contribute to technical publications or report
