Senior Platform Software Engineer, AI/ML & GPU Infrastructure

Cisco

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: San Jose, CARTP, NC
Salary: $167,700–$245,200 / yr
Employment: Full-time
Posted: 5 days ago
Freshness: Confirmed live today
Closes: Nov 16, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $189k

This role $206k

$129k $258k

below market most similar roles pay here above market

This role pays more than 61% of similar roles. Most pay $151,000–$227,000 — the blue band above. At the midpoint, this role pays about $206k versus about $189k for comparable roles.

Based on 240 similar postings.

Employer

About Cisco

Cisco Systems is the world''s leading networking technology company, designing and manufacturing networking hardware, telecommunications equipment, and cybersecurity solutions for businesses and governments. Industry: Networking Technology & Cybersecurity

Cisco currently has 307 open roles on FindRole.

Listed pay typically runs $167,700–$245,000 across 224 roles with salary data.

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At a glance

TL;DR · Senior Platform Software Engineer, AI/ML & GPU Infrastructure

Role Posting Log in to save

The Senior Platform Software Engineer joins the Platform Engineering team to build and operate the AI Compute Platform, which provides the GPU infrastructure for model training, inference, and LLM workloads. You will own the architecture and operation of control-plane and scheduling systems, developing distributed systems, APIs, and controllers to manage multi-cluster environments and enforce tenant GPU quotas. Your daily work involves designing automated capacity-intake, demand-classification, and remediation tooling while scaling Kubernetes-based platforms. You will utilize Go, Python, Terraform, and cloud-native observability tools like Splunk, Prometheus, and Grafana. The role addresses the technical challenge of turning raw GPU capacity into a reliable, self-service platform by managing complex workload orchestration, multi-tenant isolation, and scheduling logic for diverse training and inference requirements across accelerated compute platforms.

Skills

Kubernetes Go Python Infrastructure as Code Terraform GitOps CI/CD OpenShift Kubernetes Operators CRDs Prometheus Grafana Splunk NCCL eBPF Linux kernel etcd Kueue Volcano Slurm

What you'll do

Architect and develop platform control-plane services, APIs, and controllers to manage multi-cluster lifecycles and GPU quota enforcement.
Design GPU capacity scheduling logic for fractional allocation, multi-tenant isolation, and workload-aware scheduling.
Build automated tooling for capacity intake, demand classification, and request routing across compute pools.
Develop automated remediation tools to detect node failures and optimize system-wide performance for training and inference.
Design and scale reliable Kubernetes-based platforms to serve as the foundation of the AI Compute Platform.
Lead design reviews and set technical standards for Kubernetes Operators, CRDs, and automation using Go and Python.
Drive Infrastructure as Code, GitOps, and CI/CD automation while participating in on-call rotations and incident response.

What we're looking for

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.
Technical leadership experience in a software engineering environment.
Strong software development proficiency in Go or Python.
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 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, GitOps, CI/CD, and cloud-native observability tools.

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