The RoleWe are looking for engineers to help build the post-training stack for frontier reasoning models. This role sits at the intersection of research and infrastructure. You will work to push the scale of our RL stack, whether it is novel recipe ideas, reliability, or performance. Some example areas you might work on (not limited to):
- Design and build reliable infrastructure for large-scale RL training
- Implement novel performance optimizations across the training stack
- Develop evaluation and benchmarking infrastructure to measure model progress, throughput, and uptime
- Build data collection and feedback pipelines that close the loop between human signal, reward modeling, and training
- Collaborate with multiple teams to rapidly iterate on RL algorithms and get experiments into production training runs
If you're excited about building the infrastructure that makes frontier RL research possible at scale, we'd love to hear from you.
We offer a base salary of $350,000-$500,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.
