Reactor is building the inference platform for real-time world models. We run the largest and fastest catalog of world models on the market (15+ and growing), including LingBot World 2, LTX 2.5 Realtime, and HappyOyster. Our founders, Alberto Taiuti (CEO) and Bryce Schmidtchen (CTO), met as technical leads on the early Apple Vision Pro team. Alberto went on to co-found Luma AI, now a $4B company. We've raised $59M, led by Lightspeed, with Amplify Partners, WndrCo, and NVIDIA's NVentures participating. Jeffrey Katzenberg, cofounder of Dreamworks Animation, sits on our board of directors.
This role sits at the intersection of ML engineering and customer partnership. You will be the person who makes it real for our lab partners, going on-site to their offices, understanding their models deeply, and doing whatever it takes to get them running at production quality on Reactor's infrastructure.
This role sits at the intersection of ML engineering and customer partnership. You will be the person who makes it real for our lab partners, going on-site to their offices, understanding their models deeply, and doing whatever it takes to get them running at production quality on Reactor's infrastructure.
This is not a support role. You will be making critical technical and product decisions and working directly with the founders. You will be one of the first people to define what forward deployment looks like at Reactor.
What You'll Do
- Travel to top world model labs, integrate their models onto Reactor's infrastructure, and own the deployment end to end
- Profile and optimize model performance to hit the latency and throughput targets that make real-time interactive video feel real
- Be the voice of the lab partner inside Reactor and make product decisions that directly shape what we build next
What We're Looking For
- 3+ years in ML engineering or a technical role working directly with external teams
- Comfortable reading and writing Python at a production level and tracing through inference pipelines to find and fix bottlenecks
- Strong knowledge of PyTorch and modern ML inference optimization techniques
- Comfortable with ambiguity, travel, and getting on a plane to unblock a partner
Nice to Have
- Experience with diffusion models, video generation, or world models
- Familiarity with real-time streaming systems
- CUDA kernel optimization, TensorRT, or custom inference frameworks
- Existing relationships in the generative video or foundation model ecosystem
Benefits
- Competitive SF salary and meaningful early equity
- Visa sponsorship and relocation support
- Generous health, dental, and vision coverage
- Travel covered for on-site integrations