Member of Technical Staff, Applied Research

What you will do

Post-training loops. You’d help design and run model improvement workflows across supervised fine-tuning, preference optimization, and reinforcement learning approaches like GRPO. The work is not just launching training jobs; it’s figuring out what signal matters, how to collect it, and whether the model actually improved.
Environment and task design. We build environments that feel real and scenarios that push agents past static benchmark behavior. You’d design tasks, tools, validators, reward signals, and evaluation harnesses that test meaningful capabilities instead of whatever is easiest to measure.
High-fidelity trajectories. You’d create, inspect, and improve the data that teaches models how to behave. That means caring about taste, correctness, edge cases, and whether a trajectory would actually help a frontier model learn.
Reward and evaluation systems. You’d work on reward functions, rubrics, validators, and analysis tools that turn messy model behavior into useful training signal. You should be interested in where evals lie, where rewards get hacked, and how to make measurements more robust.

Post-training loops. You’d help design and run model improvement workflows across supervised fine-tuning, preference optimization, and reinforcement learning approaches like GRPO. The work is not just launching training jobs; it’s figuring out what signal matters, how to collect it, and whether the model actually improved.
Environment and task design. We build environments that feel real and scenarios that push agents past static benchmark behavior. You’d design tasks, tools, validators, reward signals, and evaluation harnesses that test meaningful capabilities instead of whatever is easiest to measure.
High-fidelity trajectories. You’d create, inspect, and improve the data that teaches models how to behave. That means caring about taste, correctness, edge cases, and whether a trajectory would actually help a frontier model learn.
Reward and evaluation systems. You’d work on reward functions, rubrics, validators, and analysis tools that turn messy model behavior into useful training signal. You should be interested in where evals lie, where rewards get hacked, and how to make measurements more robust.