Principal AI Research Engineer - Reinforcement Learning

Company Overview

Reflex Robotics builds general-purpose, AI-powered humanoid robots for the toughest jobs in industry.

We’re focused on jobs that are genuinely hard on the human body: lifting 40-pound bags of dog food, loading boxes for an entire shift, and moving heavy materials from floor to high overhead shelves. Not just lightweight tasks in controlled environments. Actual, back-breaking work.

Factory and warehouse work demands more. The hardware needs strength, reach, precision, speed, and the battery life to be useful across multiple shifts. It also needs to deliver a clear return today, not in some hypothetical future. Our robot costs $32K at development volumes today.

We build the full robot stack ourselves, from cameras and compute to actuators, electronics, and software. Our custom-built supervision stack helps handle the hard parts of deployment, from real floor variability to keeping work moving without line disruption. That supervision also generates proprietary real-world data that helps improve autonomy over time.

We are a four year old startup backed by Khosla Ventures and a team of other great investors. Our team includes alumni from Anduril, Oculus, Amazon, Waymo, Skydio, Boston Dynamics, ASML, MIT, Jane Street, and Joby Aviation.

Key Company Beliefs

• We are obsessed with shipping robots.

• A humanoid robot is only valuable if it solves a real customer problem. Cool demos are fun, but reliable work is what matters.

• Low cost is what makes widespread deployment possible.

• High-quality, proprietary robotics data is the foundation of the next generation of physical AI.

• Getting nerd-sniped by an engineering metric is way less important than solving our customers’ biggest pain points.

What We’re Looking For

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.

Come Join Us

This is a rare opportunity to help build a flagship robotics company from the ground up—and to do work that will truly matter, reshaping what people believe is possible in robotics.

We love to see the things you’ve worked on. Have a portfolio or insane project you’ve worked on? Share it. We’re looking for people who push past the status quo, are passionate at work and in their own time—we’re looking for people who want to win.