Building frontier AI evaluations, agentic benchmarks, and embodied robotics systems. I sit at the intersection of rigorous ML research and real-world deployment.
Fig. 01 — Me, photobombing an Apollo Command Module.
I build the systems that make frontier AI models better — from crafting evaluation datasets that probe real reasoning failures, to designing agentic benchmarks that stress-test long-horizon planning. Currently at Labelbox, I've helped grow one account from $0 to $25M ARR while pushing the state of RLHF and RLVR for the world's top AI labs.
On the robotics side, I spent years at Duality building closed-loop simulations, synthetic data pipelines, and digital twins using NeRFs and Cycle-GANs. I believe the best AI is shaped by people who understand both the math and the metal — the loss curves and the actuators.
Building frontier-model evaluations, RLHF/RLVR datasets, and agentic benchmarks for the world's top AI labs. Grew one account from $0 to $25M ARR. Promoted within the first year.
Custom benchmark suites inspired by AIME, HLE, SciCode, and LiveCodeBench; preference-labeling and RL-ready conversational datasets with rubric-grounded supervision.
Defined AI data strategies with enterprise customers, lifted vision-model accuracy 50%+ with synthetic data, and integrated LLMs into closed-loop robotics simulations.
Digital twins and 3D assets from NeRFs and Cycle-GANs — detailed 3D replicas from limited image sets.
Built the electronics of a 3D-scanning rig and automated software whitelisting for secure environments.
Robots, benchmarks, or a strong opinion about AI — my inbox is open.
gandharv.mahajan@gmail.com ↗