Senior Software Engineer, CoreWeave

• 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
• This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency
• Wondering if you’re a good fit? We believe in investing in our people and value candidates who can bring their diverse experiences to our teams – even if you aren't a 100% skill or experience match

Desirable

• 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
• 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