Senior Platform Software Engineer – AI/ML & GPU Infrastructure

Cisco Systems, Inc; San Jose (CA); On-site; USD 168,000 - 245,000; Full time

Job summary

Cisco Systems, Inc. is seeking a Senior Platform Software Engineer to own the control-plane and scheduling systems for the AI Compute Platform. You will work across distributed control planes, Kubernetes, and GPU workloads, guiding architecture and operations for multi-cluster environments.

You will mentor engineers, improve reliability, and drive automation across IaC, GitOps, and CI/CD, while collaborating with hardware and software teams to scale training and inference workloads.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 8+ years of software engineering experience building Kubernetes-based platforms or large-scale distributed systems.
• Proficient in Go or Python with experience building platform control-plane services and Kubernetes operators.
• Experience with batch and accelerator schedulers for GPU/AI workloads.
• Deep experience with GPU scheduling and multi-tenant infrastructure.
• Familiar with IaC, GitOps, CI/CD, and observability tools.

Responsibilities

• Architect and develop platform control-plane services, APIs, and controllers to manage multi-cluster lifecycle and GPU quota enforcement.
• Own architecture for GPU capacity scheduling and allocation across shared and dedicated pools.
• Develop automated capacity intake, demand classification, and routing tooling for workloads.
• Create remediation tooling for node and workload failures to improve reliability and throughput.
• Design, build, and scale Kubernetes-based platforms as the foundation of the AI Compute Platform.
• Lead design reviews and set standards for Kubernetes Operators, Controllers, CRDs, and Go/Python automation.
• Mentor engineers and promote best practices for cloud-native software and platform reliability.
• Drive IaC, GitOps, and CI/CD and participate in on-call rotation and postmortems.

Skills

Go

Python

Kubernetes

Distributed systems

Education

Bachelor's degree in Computer Science or Computing

Tools

Terraform

GitOps

OpenShift

Job description

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 reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits.

Individual pay is determined by the candidate’s hiring location, market conditions, job‑related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process.

U.S. employees are offered benefits, subject to Cisco’s plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long‑term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks. Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time.

U.S. employees are eligible for paid time away as described below, subject to Cisco’s policies:

• 10 paid holidays per full calendar year, plus 1 floating holiday for non‑exempt employees
• 1 paid day off for employee’s birthday, paid year‑end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco
• Non‑exempt employees** receive 16 days of paid vacation time per full calendar year, accrued at rate of 4.92 hours per pay period for full‑time employees
• Exempt employees participate in Cisco’s flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations)
• 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next
• Additional paid time away may be requested to deal with critical or emergency issues for family members
• Optional 10 paid days per full calendar year to volunteer

For non‑sales roles, employees are also eligible to earn annual bonuses subject to Cisco’s policies.

Employees on sales plans earn performance‑based incentive pay on top of their base salary, which is split between quota and non‑quota components, subject to the applicable Cisco plan. For quota‑based incentive pay, Cisco typically pays as follows:

• .75% of incentive target for each 1% of revenue attainment up to 50% of quota;
• 1.5% of incentive target for each 1% of attainment between 50% and 75%;
• 1% of incentive target for each 1% of attainment between 75% and 100%; and
• Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation.

For non‑quota-based sales performance elements such as strategic sales objectives, Cisco may pay 0% up to 125% of target. Cisco sales plans do not have a minimum threshold of performance for sales incentive compensation to be paid.

The applicable full salary ranges for this position, by specific state, are listed below:

New York City Metro Area:

$167,700.00 - $282,000.00

Non‑Metro New York state & Washington state:

$149,100.00 - $250,900.00

* For quota‑based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined.

** Employees in Illinois, whether exempt or non‑exempt, will participate in a unique time off program to meet local requirements.