Kernel Science builds Corollary Labs, a platform that puts scientific models, tools and datasets behind one API and a supervised research agent. We are looking for a master's student to measure how well it actually works: validate its outputs, benchmark it against other models, and turn the results into evidence we can publish and act on.
Corollary Labs routes scientific questions to models such as ESMFold, AlphaFold2, Boltz-2, DiffDock, ProteinMPNN and Evo 2, and lets a supervised agent chain them into an investigation. Your job is to find out, rigorously, where that pipeline is right, where it is wrong, and how it compares with alternatives.
Over six months you will perform:
a) Design benchmark suites with ground truth: structure prediction, docking, sequence and molecular property tasks, and end-to-end research questions.
b) Run Corollary Labs, competing models and general-purpose AI assistants on the same tasks, and collect results reproducibly.
c) Apply statistical analysis to the outcomes: calibration, confidence intervals, error attribution, and comparisons that hold up to scrutiny.
d) Build ML-based validation methods, for example automated scoring of agent outputs and detection of systematic failure modes.
e) Write up the findings as internal reports and, where
the results warrant it, a technical note or paper with the team.