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# Senior Software Engineer II (IC4) - AI Workload Orchestration

- **Company:** Coreweave Inc
- **Location:** Bellevue, WA, United States
- **Experience:** Expert
- **Salary:** $182,000.0 - $242,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, C++ (Programming Language), Cloud Computing, Distributed Systems, Python (Programming Language), Machine Learning, Software Engineering, AI Infrastructure, Delivery Pipeline, Reliability of Systems, Kubernetes, Information Technology
- **Published:** October 22, 2026
- **Apply:** https://www.thejobnetwork.com/job/6e4cd740-ab30-4f42-884a-93f2d1ac2457/senior-software-engineer-ii-ic-ai-workload-orchestration

## About the Role

* 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

## Description

* 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