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Reactor in San Francisco is building an inference platform for real-time world models. We are looking for engineers who will expand our robot testing fleet, deploy learned policies on robot arms, and build live demos that showcase our stack to partners.
You will work closely with researchers and engineers, design robust test setups, and develop Python tooling to automate experiments and capture reproducible results. This role is based in person in San Francisco.
Reactor is building the inference platform for real-time world models. Our goal is to democratize access to real-time intelligence to unlock next-generation use cases in media, physical AI and omni-directional models. We already run the largest and fastest catalog of world models on the market, and work closely with all major model labs to run and optimize their world models, including Alibaba, Amazon, and NVIDIA. Our team is made of alumni from leading firms such as Apple, Meta, Amazon, Microsoft, Luma AI, Adobe, Stability AI, and more. We've raised $74M led by Lightspeed, with Amplify Partners, WNDR, NVIDIA, Sapphire, and other Tier 1 investors participating. Jeffrey Katzenberg, co-founder of DreamWorks Animation, sits on our board of directors.
Robot learning moves fast, and you’ll be the one who proves a new model actually works on a real robot.
You’ll help build out our robot testing fleet, get the newest policies (vision-language-action models, imitation learning checkpoints) running on real arms, and ship the live demos that show what our software stack can do. When a trial fails, you’ll work out why: a loose camera mount, a flipped coordinate frame, a timing bug in the control loop, or a model that isn't as good as its benchmark says.
You’ll work directly with our researchers and engineers, and you’ll help decide what we test and how. As the lab grows, you’ll build the tools and procedures that make testing faster, safer, and repeatable.
We're in person in San Francisco.
You've built, run, or repaired robots and liked it enough to want to do it every day. That could be from a job, a research lab, a robotics competition, or your own garage.
Most of all, you're curious about robot learning, patient with hardware, and you push through problems on your own. You enjoy both the first time a new model moves an arm and the follow-up work of finding out how reliably it does it.