Platform Engineer

Harrison Clarke; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Harrison Clarke is seeking a Senior Platform Engineer to take ownership of their core platform in San Francisco. This position involves designing multi-region Kubernetes clusters, managing GPU infrastructure, overseeing networking systems, and developing observability across metrics and logs. The ideal candidate will have solid experience with Kubernetes in production, infrastructure-as-code tools like Terraform, and must be proficient with GitOps practices. Strong networking fundamentals and familiarity with in-memory data systems are also essential. If you have a passion for building AI systems, apply now!

Qualifications

• Strong experience operating Kubernetes in production environments, including troubleshooting, autoscaling, and upgrades.
• Proven background with infrastructure-as-code tools.
• Hands-on experience running GPU workloads on Kubernetes.

Responsibilities

• Design and manage multi-region Kubernetes clusters across cloud and GPU-focused providers.
• Own the deployment lifecycle through GitOps practices.
• Manage GPU infrastructure, including scheduling efficiency and workload placement.

Skills

Kubernetes

Infrastructure-as-code tools

GPU workloads optimization

GitOps tooling

In-memory data systems

Observability tooling

Networking fundamentals

Tools

Terraform

Helm

Pulumi

Redis

Job description

Overview

Our client, an early-stage company building advanced AI systems, is seeking a senior platform engineer to take ownership of their core platform. This is not a traditional DevOps position focused purely on CI/CD; the role spans GPU orchestration, multi-cloud Kubernetes environments, real-time networking, and observability. The company is already running production workloads across multiple clusters, regions, and hardware types, and is actively expanding into additional cloud providers. This hire will play a key role in scaling and stabilizing that infrastructure.

Key Responsibilities

• Design and manage multi-region Kubernetes clusters across cloud and GPU-focused providers using infrastructure-as-code
• Own the deployment lifecycle through GitOps practices (Helm, Kustomize, automated releases, continuous delivery)
• Manage GPU infrastructure, including scheduling efficiency, workload placement, and cold-start optimization
• Oversee networking systems such as ingress, gateways, load balancing, and cross-region connectivity
• Build and maintain observability across metrics, logs, traces, and performance profiling
• Ensure infrastructure security across identity, secrets, and encryption
• Maintain CI/CD workflows supporting a monorepo of services and deployment artifacts
• Partner closely with ML engineers to optimize model serving and GPU utilization

Candidate Profile

• Strong experience operating Kubernetes in production environments, including troubleshooting, autoscaling, and upgrades
• Proven background with infrastructure-as-code tools (e.g., Terraform, Pulumi)
• Hands-on experience running GPU workloads on Kubernetes and understanding resource optimization
• Familiarity with GitOps tooling such as ArgoCD or Flux, and Helm-based deployments
• Experience with in-memory data systems (e.g., Redis) and distributed architectures
• Solid understanding of observability tooling and practices
• Strong networking fundamentals, particularly in low-latency or distributed systems
• Experience working in environments with broad ownership across infrastructure

Preferred Background

• Exposure to GPU cloud providers beyond major hyperscalers
• Experience with real-time or streaming infrastructure
• Proficiency in Go or Python
• Familiarity with ML model deployment and optimization
• Experience managing infrastructure cost, particularly for GPU-heavy workloads