We’re looking for stellar on-policy RL engineers to work on creating robust robot policies.
We’re still a small team—which means high ownership, high equity, and the chance to shape the product from the ground up.
VLAs and other great “base policies” for robotics achieve ~80% success rates, but in real robot deployments, it’s essential to achieve 99.99% success rates. We can’t ask our customers to tolerate our robots packing three socks into a bin instead of four, or swapping shipping labels between two packages—not even once!
You should apply for this role if
- You’ve re-implemented core RL algorithms (SAC, DDPG) from scratch and can debug unstable gradients / tune hyperparameters correctly
- You’ve made meaningful intellectual contributions to sample-efficient RL algorithms (e.g., DreamerV3 and MuZero)
- You’ve shipped on-policy RL on hardware that learns in the real-world (e.g., for quadruped walking or drone racing)
You’d be joining a company that already has a solid core business—with working hardware, delighted customers, and profitable unit economics. Reflex is de-risked enough to see the hazy outlines of success, but still small enough that there’s enormous upside up for grabs.