Platform Engineer
Reactor
San Francisco, CA · Posted October 30, 2026
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About the job
Platform Engineer
Department: Engineering
Employment Type: Full Time
Location: San Francisco
Description 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.
We're looking for a senior cloud and platform engineer who builds the foundation everything at Reactor runs on, and owns keeping it up.
That foundation is big for our size. Production runs across clouds, in regions from São Paulo to Tokyo, on many types of GPUs. Every new model and every new customer lands on it.
This isn't a pure DevOps or SRE seat. You'll design systems and write real code, and you'll also own the deploys, the on-call and the cleanup. Our ML Infrastructure team builds the inference scheduling and serving layer on top of what you run.
We're in person in San Francisco.
What You'll Work On
New clouds and regions. Bringing up GPU providers and regions in Terraform, and making the next one a copy-paste instead of a project
Kubernetes across all of it. Cluster lifecycle, autoscaling (HPA, VPA, node pools), capacity fallbacks when a region runs out of GPUs
The real-time network. Ingress, load balancing, our WebRTC media relay, and geo-routing users to the closest GPU
Data and messaging. Kafka and Redis running across regions, with alerting that catches problems before customers do
Ship path. Flux GitOps, Helm charts, image automation, and CI/CD for a Go and Python monorepo
Observability. Grafana, Prometheus, OpenTelemetry and eBPF tracing across services and GPU workloads
What We're Looking For
6+ years building and running production infrastructure, with real scale behind it
You've operated Kubernetes in production, not just deployed to it: debugged scheduling, tuned autoscalers, run cluster upgrades
Strong infrastructure as code (Terraform, Pulumi or similar) across many environments and regions
Solid distributed systems fundamentals, and you write production code in Go or Python
Networking: load balancers, TLS, DNS, NAT, cross-region latency
You've owned infrastructure end to end at a startup
Systems judgment (failure modes, blast radius, cost)
Nice to Have
GPU workloads on Kubernetes: device plugins, GPU-aware scheduling, bin-packing expensive hardware
GPU clouds beyond AWS
Kafka or other streaming systems at scale
Real-time media, WebRTC or low-latency networking
GPU cost and capacity planning
Benefits
Salary and equity at the 99th percentile for our stage
Visa sponsorship and relocation support
Generous health, dental, and vision coverage