Platform and software · shared across customers
Reports to: Director, Platform Engineering (or Chief Architect)
Location: Remote (US) or Pleasanton, CA (hybrid)
Department: Cloud Platform Engineering / GPU Platform Engineering
Position summaryThe Senior Platform Engineer builds and operates the multi-tenant orchestration, scheduling, and customer-facing platform layer that turns raw GPU infrastructure into a usable cloud service. This role is the software backbone of GPU One (GPUaaS).
Key responsibilitiesDesign and build the orchestration layer (Kubernetes, Slurm, Run:ai, or comparable)
Manage multi-tenant isolation including namespaces, networking, storage, and quotas
Build customer-facing platform APIs, CLIs, web portals, and SDKs
Implement and operate image management, GPU operator, and node provisioning automation
Drive infrastructure-as-code and automation across the platform stack
Partner with SRE on platform reliability, SLO definition, and observability
Support TAM and Support engineers on customer-impacting platform issues
Maintain customer environment templates, configuration management, and rollout tooling
Participate in architecture review, design discussions, and technical roadmap
Drive continuous platform improvement and reduce operational toil
6+ years in platform engineering, SRE, or cloud engineering at scale
Deep Kubernetes expertise including CRDs, operators, and multi-tenant patterns
Strong programming skills in Go, Python, or both
Experience operating GPU clusters or AI infrastructure at production scale
Bachelor's degree in computer science or equivalent experience
Experience with NVIDIA GPU Operator, MIG, MPS, and NCCL operator patterns
Familiarity with Slurm operator, Run:ai, KubeRay, or comparable AI orchestration
Service mesh experience (Istio, Linkerd) and multi-cluster networking
Open source contributions in the cloud-native or AI infrastructure ecosystem
Skills Required
- 6+ years of experience in platform engineering, SRE, or cloud engineering at scale
- Deep Kubernetes expertise, including CRDs, operators, and multi-tenant patterns
- Strong programming skills in Go, Python, or both
- Experience operating GPU clusters or AI infrastructure at production scale
- Bachelor's degree in computer science or equivalent experience
- Experience with NVIDIA GPU Operator, MIG, MPS, and NCCL operator patterns
- Familiarity with Slurm operator, Run:ai, KubeRay, or comparable AI orchestration
- Service mesh experience with Istio, Linkerd, or similar and multi-cluster networking
- Open source contributions in the cloud-native or AI infrastructure ecosystem
What We Do
STN, Inc. is a managed technology and infrastructure provider serving enterprise, regulated, and AI-driven organizations. It designs, operates, and supports secure, scalable systems, including managed IT, cloud and platform services, cybersecurity, data management, compliance engineering, enterprise hardware and software, and GPU One, its GPU-as-a-Service platform for AI training, tuning, inference, and other high-performance workloads. STN emphasizes reliability, audit readiness, and ongoing human support.
