Senior Platform Software Engineer – AI/ML & GPU Infrastructure
About The Position
Join Cisco's Platform Engineering team to build and operate the AI Compute Platform, the GPU infrastructure for model training, inference, and LLM workloads at scale. The team develops and operates the unified control plane and Kubernetes platform, manages GPU capacity across shared and dedicated pools, and works with infrastructure, hardware, and engineering teams to deliver reliable, self-service GPU resources. In this role, you will own the architecture, technical direction, and operation of control-plane and scheduling systems for GPU infrastructure. You will design and develop distributed systems, control-plane services, and APIs for multi-cluster environments, tenant GPU quotas, and complex training and inference workloads. You will apply systems software engineering, API design, and operational leadership to improve technical standards, mentor engineers, and maximize reliability and throughput across the AI compute fleet.
Requirements
- 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, with experience architecting platform control-plane services, custom APIs, Kubernetes Controllers, Operators, or CRDs.
- Hands-on experience building, architecting, or operating batch and accelerator schedulers for GPU or AI/ML workloads, such as Kueue, Volcano, Slurm, or custom scheduler plugins.
- Deep experience with GPU scheduling, multi-tenant GPU infrastructure, or workloads on accelerated compute platforms.
- Experience designing for reliability and operational excellence using Infrastructure as Code such as Terraform, GitOps, CI/CD, and cloud-native observability tools such as Splunk, Prometheus, or Grafana.
Nice To Haves
- Experience scaling Kubernetes control-plane internals (API server, etcd, controller-manager, or scheduler framework) or multi-cluster federation at large node and object counts.
- Familiarity with modern data center GPU architectures such as NVIDIA H100, H200, B200/B300, or AMD Instinct accelerators, and their scheduling, placement, and interconnect considerations such as NVLink and InfiniBand.
- Familiarity with topology-aware placement, collective-communication libraries such as NCCL, and gang scheduling for large-scale multi-node training clusters.
- Experience supporting AI/ML infrastructure, including model serving pipelines, distributed training, workload orchestration, or MLOps platforms.
- Low-level systems engineering experience, such as Linux kernel tuning, cgroups, or eBPF.
- Experience leading ambiguous platform initiatives end-to-end and representing platform architecture in cross-team design review forums.
- Experience participating in on-call rotations, incident response, and postmortem or runbook processes for production infrastructure.
Responsibilities
- Architect and develop platform control-plane services, APIs, and controllers for multi-cluster lifecycle management, tenant project onboarding, and GPU quota enforcement across shared and dedicated pools.
- Own the technical architecture for GPU capacity scheduling and allocation, including fractional GPU allocation, multi-tenant isolation, and workload-aware scheduling for training and inference.
- Design and develop automated capacity-intake, demand-classification, and request-routing tools to direct workloads to appropriate clusters and compute pools.
- Architect automated remediation tools that detect node and workload failures, trigger remediation, and optimize system-wide performance.
- Design, build, and scale Kubernetes-based platforms using OpenShift or upstream Kubernetes.
- Lead design reviews and set technical standards for platform services, Kubernetes Operators, Controllers, CRDs, and Go- and Python-based automation.
- Mentor engineers and promote strong cloud-native software engineering practices and platform reliability.
- Drive Infrastructure as Code, GitOps, and CI/CD automation for the platform.
- Participate in on-call rotation, incident response, and postmortem reviews.
Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- 401(k) plan with Cisco matching contribution
- Paid parental leave
- Short- and long-term disability coverage
- Basic life insurance
- Paid holidays
- Floating holiday for non-exempt employees
- Paid birthday day off
- Paid year-end holiday shutdown
- Personal wellness days
- Paid vacation time for non-exempt employees
- Flexible vacation time off program for eligible exempt employees
- Sick time off
- Additional paid time away may be requested for critical or emergency family issues
- Optional paid volunteer days
- Annual bonuses for eligible non-sales employees
- Potential grants of Cisco restricted stock units
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What This Job Offers
Job Type
Full-time
Career Level
Senior
