Platform Engineer

Reactor; San Francisco (CA); On-site; USD 190,000 - 260,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation assistance

Health, dental, vision

Equity

Job summary

Reactor in San Francisco is seeking an infrastructure lead to own the platform our AI models run on. You’ll manage multi-region Kubernetes clusters across cloud providers, implement IaC, and drive GPU orchestration for fast, reliable model serving.

You’ll build a robust observability and networking stack, own security and CI/CD for Go and Python services, and partner with ML engineers to optimize containerized workloads in a high-growth startup.

Qualifications

• Experience operating Kubernetes at production scale, including node-level debugging and upgrades.
• Proficient with infrastructure-as-code across environments and regions.
• Experience with GPU workloads on Kubernetes and GPU-aware scheduling.

Responsibilities

• Provision and manage multi-region Kubernetes clusters across AWS and GPU clouds using IaC.
• Own the GitOps deployment lifecycle (Helm, Kustomize, image automation, CD).
• Manage GPU node infrastructure: scheduling, caching, prefetching, observability.

Skills

Kubernetes at scale

Infrastructure as code

GPU workloads on Kubernetes

GitOps tooling

Helm chart authoring

Observability stack

Networking fundamentals

End-to-end infra ownership

Go or Python proficiency

Container orchestration

Tools

Terraform

Pulumi

FluxCD

ArgoCD

Kustomize

Helm

Redis

Job description

Description

You'll own the infrastructure platform that our AI models run on. This isn't a CI/CD-focused DevOps role. You'll work across GPU orchestration, multi-cloud Kubernetes, real-time networking, and observability. You'll be the person who knows why a model pod took 4 minutes to schedule, why cross-region latency spiked, or why a media relay is dropping packets.

Department

Engineering

Location

San Francisco

We run production today across multiple Kubernetes clusters, regions, and GPU types, and we're actively expanding to additional cloud providers. You'll lead that expansion and keep everything running.

What You'll Do

• Provision and manage multi-region Kubernetes clusters across AWS and GPU cloud providers using infrastructure-as-code.
• Own the GitOps deployment lifecycle (Helm charts, Kustomize overlays, image automation, and continuous delivery.)
• Manage GPU node infrastructure: scheduling, model weight caching, image prefetching for fast cold starts, and GPU observability.
• Operate and improve our networking layer: ingress and gateway management, load balancing, media relay infrastructure, and cross-region connectivity.
• Build and maintain our observability stack: metrics, logs, traces, and profiling across all services and GPU workloads.
• Maintain infrastructure security: IAM, secret management, certificate automation, and encryption at rest.
• Own CI/CD pipelines for monorepo builds spanning Go services, Python model containers, and Helm chart releases.
• Partner with ML engineers on model serving: container optimization, health checks and startup tuning, media pipeline performance, and multi-GPU configuration.

What We're Looking For

• You've operated Kubernetes in production at scale, not just deployed to it, but debugged node-level scheduling issues, tuned autoscalers, and managed cluster upgrades.
• Strong infrastructure-as-code experience (Terraform, Pulumi, or similar) across multiple environments and regions.
• You've worked with GPU workloads on Kubernetes: device plugins, node taints/tolerations, GPU-aware scheduling. You understand why bin-packing matters for expensive hardware.
• Experience with GitOps tooling (FluxCD, ArgoCD, or similar) and Helm chart authoring.
• Comfortable with Redis or similar in-memory data stores (replication, persistence, pub/sub or streaming patterns)
• Familiarity with modern observability stacks (Prometheus, Grafana, OpenTelemetry, or equivalent) and knowing when to reach for metrics vs. logs vs. traces.
• Solid networking fundamentals: load balancers, TLS, DNS, NAT. Real-time or low-latency networking experience is a strong plus.
• You've worked in a startup where you owned infrastructure end-to-end, not just one slice of it.

Nice to Have

• Experience with GPU cloud providers beyond AWS (Crusoe, CoreWeave, Lambda Labs, Nebius)
• Real-time media or streaming infrastructure
• Go or Python proficiency
• Familiarity with ML model serving (container image optimization, weight loading, GPU driver and runtime management)
• FinOps and GPU cost optimization

What We're Not Looking For

• Pure CI/CD pipeline engineers who haven't operated Kubernetes clusters directly
• Candidates whose infrastructure experience is limited to managed PaaS (Heroku, Vercel, Railway)
• People who need a fully defined scope, this role requires figuring out what to build next, not just executing tickets

Benefits

• Competitive San Francisco salary and meaningful equity
• We sponsor visas and support relocation to the US
• Generous health, dental, and vision coverage