Senior Platform Software Engineer – AI/ML & GPU Infrastructure

What you'll do

• Build control-plane services, APIs, and controllers for multi-cluster management, tenant onboarding, and GPU quota enforcement.
• Own GPU capacity scheduling, workload routing, and automated remediation for training and inference workloads.
• Set technical standards, mentor engineers, and drive platform reliability, Infrastructure as Code, GitOps, CI/CD, and incident response.

What they ask for

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 8+ years of software engineering experience building, architecting, or extending Kubernetes-based platforms or large-scale distributed systems, including technical leadership experience.
• Strong proficiency in Go or Python and experience architecting platform control-plane services, custom APIs, Kubernetes Controllers, Operators, or CRDs.
• Hands-on experience with batch and accelerator schedulers for GPU or AI/ML workloads, deep GPU scheduling or multi-tenant GPU infrastructure experience, and reliability practices using Infrastructure as Code, GitOps, CI/CD, and observability tools.

Nice to have

• Experience scaling Kubernetes control-plane internals or multi-cluster federation; familiarity with modern data center GPU architectures and topology-aware placement.
• Experience with AI/ML infrastructure, low-level systems engineering, or leading platform initiatives and production operations.

Pay

$167,700.00 to $245,200.00 USD base salary; $167,700.00 - $282,000.00 USD base salary (New York City Metro Area); $149,100.00 - $250,900.00 USD base salary (Non-Metro New York state & Washington state)

Tools

Kubernetes, OpenShift, Go, Python, Kueue, Volcano, Slurm, Terraform

AI focus

Builds and operates GPU infrastructure and scheduling systems for AI/ML model training, inference, and LLM workloads, including workload-aware placement and orchestration.