Platform Software Engineer – AI​/ML & GPU Infrastructure

The application window is expected to close on: 12/27/2026

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  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 Platform Software Engineer on the Platform Engineering team, you will design, build, and operate the control-plane and scheduling systems that make GPU infrastructure usable  will work at the intersection of distributed control planes, Kubernetes, GPU acceleration, and workload  this role, you will build and operate distributed systems, control-plane services, and APIs that manage multi-cluster environments, enforce tenant GPU quotas, and monitor complex training and inference workloads.

Your work will blend systems software engineering, API development, and operations to maximize reliability and throughput across our AI compute fleet.

Responsibilites:

Design 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.

Implement 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.

Build automated capacity-intake, demand-classification, and request-routing tooling that routes workloads to the right cluster and compute pool.

Build automated remediation tooling that detects node and workload failures, triggers automated remediation, and helps optimize system-wide performance for training and inference workloads.

Design, build, and maintain scalable, reliable Kubernetes-based platforms (Open Shift or upstream Kubernetes) as the foundation of the AI Compute Platform.

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 platform visibility, efficiency, and reliability.

Implement Infrastructure as Code, Git Ops, and CI/CD automation for the platform; participate in on-call rotation.

Minimum Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
5+ years of software engineering experience building, operating, or extending Kubernetes-based platforms or distributed systems.

Strong software development 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 (e.g., 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 (e.g., Terraform), Git Ops, CI/CD deployment pipelines, and cloud-native observability tools (e.g., Splunk, Prometheus, Grafana).

Preferred Qualifications:

Familiarity with multi-cluster control plane architectures, cluster registration/federation, and large-scale tenant isolation models.

Familiarity with…