Senior Platform Software Engineer – AI/ML & GPU Infrastructure

Role details

The application window is expected to close on: 11/29/2026 Meet the Team You'll join the Platform Engineering team building and operating Cisco's AI Compute Platform — the GPU infrastructure that powers model training, inference, and LLM workloads at scale. The Platform Engineering team builds and runs the unified control plane and Kubernetes platform that serve as the foundation of this infrastructure, owns how GPU capacity is scheduled and allocated across shared and dedicated pools, and partners closely with infrastructure, hardware, and engineering teams to turn raw GPU capacity into a reliable, self-service platform for every team that needs it. Your Impact As a Senior Platform Software Engineer on the Platform Engineering team, you will own the architecture, technical direction, and operation of the control-plane and scheduling systems that make GPU infrastructure usable at scale. You will work at the intersection of distributed control planes, Kubernetes, GPU acceleration, and workload orchestration. In this foundational role, you will architect and develop distributed systems, control-plane services, and APIs that manage multi-cluster environments, enforce tenant GPU quotas, and scale complex training and inference workloads. You will combine deep systems software engineering, API design, and operational leadership to raise technical standards, mentor engineers, and maximize reliability and throughput across our AI compute fleet. Responsibilities: Architect and develop platform control-plane services, APIs, and controllers to manage multi-cluster lifecycle, 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 logic that accounts for training (checkpointing, preemption tolerance) vs. inference (latency sensitivity, multi-node placement) differences. Drive the design and development of automated capacity-intake, demand-classification, and request-routing tooling that routes workloads to the right cluster and compute pool. Architect automated remediation tooling that detects node and workload failures, triggers automated remediation, and optimizes system-wide performance for training and inference workloads. Design, build, and scale reliable Kubernetes-based platforms (OpenShift or upstream Kubernetes) as the foundation of the AI Compute Platform. Lead design reviews and set technical standards for platform services, Kubernetes Operators, Controllers, CRDs, and automation built with Go and Python. Mentor engineers across the team, raising the bar on 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. Minimum Qualifications: 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 software development proficiency in Go or Python, with a proven track record architecting platform control-plane services, custom APIs, Kubernetes Controllers, Operators, or CRDs. Hands-on experience building, architecting, or operating batch and accelerator schedulers (e.g., Kueue, Volcano, Slurm, or custom scheduler plugins) for GPU or AI/ML workloads. Deep experience with GPU scheduling, multi-tenant GPU infrastructure, or managing workloads on accelerated compute platforms. Proven track record designing for reliability and operational excellence with Infrastructure as Code (e.g., Terraform), GitOps, CI/CD, and cloud-native observability tools (e.g., Splunk, Prometheus, Grafana). Preferred Qualifications: Experience scaling Kubernetes control plane internals (API server, etcd, controller-manager, scheduler framework) or multi-cluster federation at large node and object counts. Familiarity with modern data center GPU architectures (e.g., NVIDIA H100, H200, B200/B300, or AMD Instinct accelerators) and their scheduling, placement, and interconnect considerations (NVLink, InfiniBand). Familiarity with topology-aware placement, collective-communication libraries (e.g., 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 (e.g., 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/runbook processes for production infrastructure. Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you. Message to applicants applying to work in the U.S. and/or Canada: The starting salary range posted for this position is $167,700.00 to $245,200.00 and reflec