About Hillclimb
Hillclimb's mission is to improve agent capabilities towards recursive self-improvement. To do so, we work on creating environments and evals that test and push frontier models' ability to ideate, experiment, and produce impactful research.
We're a founding team of former DeepMind & quant researchers, backed by Tier 1 VCs, Jeff Dean, Paul Graham, Amjad Masad, and angel investors from OpenAI, Anthropic, DeepMind, xAI & Meta Superintelligence Labs.
About this role:
Your work will consist of the following and many more:
Turn ML research problems into environments that reward a model for real progress and good research taste
Test whether each environment's reward is repeatable and tracks the quality of the work it scores
Build systems that create environments at scale and catch broken ones before they reach training
Run post-training experiments to find which environments are trainable
Preferred Experience:
Prior experience with LLMs, RL, RLHF/RLAIF, RLVR, post-training, evals, graders, synthetic data, model training, or coding agents at a frontier/data lab
Understanding of what makes a reward evaluation trustworthy, and how models exploit one that is not
Background in designing automated systems for building, validating, and quality checking environments.
Benefits:
Health, dental, vision insurance
Competitive 401k match
Free lunch & dinner