Platform Software Engineer – AI/ML & GPU Infrastructure

Cisco•San Jose, CA

21h

About The Position

Join Cisco's Platform Engineering team to build and operate the AI Compute Platform's GPU infrastructure for model training, inference, and LLM workloads at scale. The team develops the unified control plane and Kubernetes platform, manages GPU capacity across shared and dedicated pools, and partners with infrastructure, hardware, and engineering teams to provide a reliable, self-service platform. As a Platform Software Engineer, you will design, build, and operate control-plane and scheduling systems for GPU infrastructure. You will work across distributed control planes, Kubernetes, GPU acceleration, and workload orchestration, developing services and APIs for multi-cluster environments, tenant GPU quotas, and complex training and inference workloads. The role combines systems software engineering, API development, and operations to improve reliability and throughput across the AI compute fleet.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
At least 5 years of software engineering experience building, operating, or extending Kubernetes-based platforms or distributed systems.
Strong proficiency in Go or Python, with hands-on experience building platform control-plane services, custom APIs, Kubernetes Controllers, Operators, or CRDs.
Hands-on experience building, configuring, or operating batch and accelerator schedulers, such as Kueue, Volcano, Slurm, or custom scheduler plugins, for GPU or AI/ML workloads.
Practical experience with GPU scheduling, multi-tenant GPU infrastructure, or managing workloads on accelerated compute platforms.
Hands-on experience with Infrastructure as Code such as Terraform, GitOps, CI/CD deployment pipelines, and cloud-native observability tools such as Splunk, Prometheus, or Grafana.

Nice To Haves

Familiarity with multi-cluster control-plane architectures, cluster registration or federation, and large-scale tenant isolation models.
Familiarity with modern data center GPU architectures, such as NVIDIA H100, H200, B200/B300, or AMD Instinct accelerators, and their scheduling and placement considerations.
Familiarity with topology-aware placement, collective-communication libraries such as NCCL, and gang scheduling for multi-node training clusters.
Experience supporting AI/ML infrastructure, including model serving pipelines, distributed training, workload orchestration, or MLOps platforms.
Experience building automated capacity-intake, demand-classification, or request-routing tools for infrastructure capacity management.
Experience developing engineering-focused developer tools, CLI utilities, or dashboards that simplify complex platform operations.
Exposure to Kubernetes control-plane internals, including the API server, etcd, controller-manager, or scheduler framework.
Experience participating in on-call rotations, incident response, and postmortem or runbook processes for production infrastructure.

Responsibilities

Design 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.
Implement GPU capacity scheduling and allocation, including fractional GPU allocation, multi-tenant isolation, and workload-aware scheduling that accounts for differences between training and inference workloads.
Build automated capacity-intake, demand-classification, and request-routing tools to direct workloads to appropriate clusters and compute pools.
Build automated remediation tools that detect node and workload failures, trigger remediation, and help optimize system-wide performance.
Design, build, and maintain scalable, reliable Kubernetes-based platforms using OpenShift or upstream Kubernetes.
Develop platform services and automation using Kubernetes Operators, Controllers, CRDs, Go, Python, and modern software engineering practices.
Develop platform automation, internal tools, and dashboards to improve visibility, efficiency, and reliability.
Implement Infrastructure as Code, GitOps, and CI/CD automation for the platform.
Participate in an on-call rotation.

Benefits

Medical, dental, and vision insurance, subject to plan eligibility rules.
401(k) plan with a Cisco matching contribution.
Paid parental leave.
Short- and long-term disability coverage.
Basic life insurance.
Restricted stock unit grants may be available, subject to eligibility and vesting conditions.
Paid holidays and a floating holiday for non-exempt employees.
Paid birthday day off, year-end holiday shutdown, and personal wellness days.
Paid vacation or flexible vacation time off, depending on employee classification.
Sick time off.
Additional paid time away may be requested for critical or emergency family issues.
Optional paid volunteer days.
Annual bonuses may be available for non-sales roles, subject to Cisco policies.