Engineering Manager, HPC Kubernetes Platform

Engineering Manager, AI Compute Platform (CaaS / GPUaaS)

Location: Dallas, TX (Relocation available)

Type: Direct Hire

• Competitive base salary + performance bonus
• 100% company-paid benefits

Overview

We are seeking an Engineering Manager, AI Compute Platform (CaaS / GPUaaS) to lead the design, scaling, and operational excellence of a next-generation compute platform delivering GPU-as-a-Service (GPUaaS) and Container-as-a-Service (CaaS) capabilities.

This platform serves as the backbone for large-scale AI/ML training, LLM workloads, and HPC applications , enabling customers to consume high-performance, GPU-accelerated infrastructure in a flexible, multi-tenant model across distributed data center environments.

You will lead a team responsible for building a bare-metal Kubernetes platform optimized for GPU workloads , ensuring high availability, performance, and efficient resource utilization at scale. This role sits at the intersection of Kubernetes, GPU infrastructure, HPC, and cloud-like service delivery models .

This is a hands-on leadership role focused on platform architecture, performance engineering, and automation , with direct impact on how GPU compute is delivered as a scalable service.

Key Responsibilities

Leadership & Team Development

• Lead, mentor, and grow a team of engineers building next-generation AI compute platforms
• Foster a culture of ownership, reliability, and continuous improvement
• Drive alignment across platform, infrastructure, and product teams

Platform Architecture – GPUaaS / CaaS

• Architect and scale a bare-metal Kubernetes platform delivering GPUaaS and CaaS capabilities
• Design multi-tenant compute environments with strong isolation, scheduling, and resource governance
• Define service models for GPU consumption, including workload orchestration, tenancy, and quota management

GPU Platform & Workload Optimization

• Optimize GPU scheduling, sharing, and utilization across large-scale clusters (MIG, device plugins, scheduler extensions)
• Support AI/ML training, LLM workloads, and HPC use cases with high throughput and low latency
• Ensure efficient workload placement across hybrid orchestration models (Kubernetes + HPC schedulers)

Automation, SRE & Platform Operations

• Drive Infrastructure-as-Code (Terraform, Ansible) and GitOps-based CI/CD practices
• Implement SRE principles across observability, reliability, and incident response
• Build automated workflows for cluster provisioning, scaling, and lifecycle management

Performance, Reliability & Capacity Planning

• Own platform performance across thousands of GPU/CPU nodes
• Define and track KPIs for utilization, latency, throughput, and system health
• Lead capacity planning aligned with rapid AI compute demand growth

Cross-Functional & Ecosystem Collaboration

• Partner with storage and networking teams to integrate distributed filesystems and high-speed interconnects (InfiniBand, RoCE)
• Collaborate with hardware and software vendors (e.g., NVIDIA ecosystem) to optimize platform capabilities
• Align platform architecture with evolving AI infrastructure and GPUaaS service offerings

Required Experience

• 7+ years of experience in platform engineering, infrastructure engineering, or SRE, with 2+ years in leadership
• Proven experience building or operating Kubernetes platforms for GPU-intensive workloads (AI/ML or HPC)
• Experience designing or supporting CaaS, GPUaaS, or multi-tenant compute platforms
• Deep understanding of GPU scheduling, workload orchestration, and resource isolation
• Strong expertise in Linux systems, networking, and performance engineering on bare-metal infrastructure
• Experience managing large-scale, distributed compute environments
• Strong experience with automation tools (Terraform, Ansible) and observability stacks (Prometheus, Grafana, Loki)
• Excellent leadership and communication skills

Preferred Experience

• Experience with NVIDIA ecosystem (GPU Operator, DCGM, MIG, device plugins)
• Familiarity with HPC schedulers (Slurm, Flux, Volcano) and hybrid orchestration models
• Experience with container runtimes (containerd, CRI-O) and Kubernetes internals
• Contributions to open-source Kubernetes, AI infrastructure, or HPC platforms
• Experience operating in hyperscale or AI-first infrastructure environments

Why This Role

• Direct ownership of a GPUaaS / CaaS platform at scale
• Work at the forefront of AI infrastructure and high-performance compute
• Opportunity to define how GPU compute is delivered as a service in next-generation environments
• High visibility role with impact across platform, product, and customer experience