CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com.

About the Role

As a Senior Software Engineer II (IC4) on the AI Workload Orchestration Platform team, you will help build and operate CoreWeave’s Kubernetes-native platform for admitting, scheduling, and operating AI workloads at scale.

This platform integrates multiple orchestration and scheduling frameworks such as Kueue, Volcano, and Ray to support modern AI training and inference workflows. It complements SUNK (Slurm on Kubernetes) by providing a Kubernetes-first, cloud-native orchestration layer with deep platform integration.

You will own meaningful components of the platform, drive reliability and performance improvements, and help scale the system as customer demand and workload complexity continue to grow.

What You’ll Do

Design, build, and operate Kubernetes-native services for AI workload orchestration and scheduling
Own one or more platform components end-to-end, including design, implementation, testing, and on-call support
Improve scheduling latency, cluster utilization, and workload reliability through metrics-driven engineering
Contribute to architectural discussions across services and influence design decisions within the platform
Work closely with adjacent teams (CKS, infrastructure, managed inference) to ensure clean interfaces and integrations
Mentor junior engineers and raise the quality bar for code, design, and operations

Who You Are

5–8 years of professional software engineering experience in distributed systems, cloud infrastructure, or platform engineering
Strong experience building production systems in Go (Python or C++ a plus)
Solid understanding of Kubernetes fundamentals, APIs, controllers, and operating services in production
Experience working with scheduling, resource management, or quota-based systems
Proven ability to improve system reliability and performance using data and operational metrics
Comfortable owning services in production and participating in on-call rotations
Bachelor’s Degree in Computer Science, Engineering or related technical field.

Preferred Qualifications

Experience with Kubernetes-native orchestration frameworks such as Kueue, Volcano, Ray, Kubeflow, or Argo Workflows
Familiarity with GPU-based workloads, ML training, or inference pipelines
Knowledge of scheduling concepts such as quota enforcement, pre-emption, and backfilling
Experience with reliability practices including SLOs, alerting, and incident response
Exposure to AI infrastructure, HPC, or large-scale distributed compute environments

The base salary range for this role is $182,000.00 - $242,000.00. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

What We Offer

The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location.

In addition to a competitive salary, we offer a variety of benefits to support your needs. The benefits below reflect our US-based offerings for full-time employees; for roles in other locations, benefits vary and are shared during the hiring process. These include:

Medical, dental, and vision insurance - 100% paid for by CoreWeave