Platform Software Engineer – AI/ML & GPU Infrastructure
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, schedules and allocates GPU capacity across shared and dedicated pools, and collaborates with infrastructure, hardware, and engineering teams to provide reliable, self-service GPU resources. The Platform Software Engineer designs, builds, and operates control-plane and scheduling systems that make GPU infrastructure usable at scale. The role spans distributed control planes, Kubernetes, GPU acceleration, and workload orchestration, including services and APIs for multi-cluster environments, tenant GPU quotas, and training and inference workloads. The work 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 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 production infrastructure postmortem or runbook processes.
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 for 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 to detect node and workload failures, trigger remediation, and help optimize performance for training and inference workloads.
- 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 insurance
- Dental insurance
- Vision insurance
- 401(k) plan with Cisco matching contribution
- Paid parental leave
- Short- and long-term disability coverage
- Basic life insurance
- Cisco restricted stock unit grants may be available, subject to eligibility and vesting conditions
- Paid holidays
- Floating holiday for non-exempt employees
- Paid birthday day off
- Paid year-end holiday shutdown
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
