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
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