Member of Technical Staff - Environments & Post-Training

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