Staff Software Engineer (AI Workload Orchestration)

As a Staff Software Engineer (IC5) on the AI Workload Orchestration Platform team, you will act as a technical leader for CoreWeave’s Kubernetes-native orchestration strategy for AI workloads

You will define and evolve the architecture for how AI workloads are admitted, scheduled, and governed across large GPU clusters using frameworks such as Kueue, Volcano, and Ray

This platform serves as a strategic complement to SUNK (Slurm on Kubernetes) and underpins both training and inference workloads across the CoreWeave cloud

This role requires strong systems thinking, cross-team influence, and a long-term view of platform scalability, reliability, and developer experience

Own the technical vision and architecture for major portions of the AI Workload Orchestration Platform

Design scalable, reliable orchestration primitives for AI workloads across multiple schedulers and runtimes

Lead cross-team architecture reviews and drive alignment across infrastructure, CKS, and managed inference teams

Define platform standards for reliability, observability, capacity management, and operational excellence

Identify and resolve systemic performance, scalability, and fairness issues across large GPU clusters

Mentor senior engineers and grow technical leadership within the organization

Represent the platform in technical reviews and influence broader CoreWeave platform strategy- Strong proficiency in Go and experience designing large-scale, long-lived production systems

Deep knowledge of Kubernetes internals, scheduling mechanisms, and controller-based architectures

8+ years of professional software engineering experience, with deep expertise in distributed systems or cloud platforms

Strong operational mindset with experience owning mission-critical systems at scale

Proven ability to lead technical initiatives across teams without direct authority

Demonstrated experience designing or evolving orchestration, scheduling, or resource-management platforms

Contributions to open-source infrastructure or orchestration projects

Experience defining and operating SLOs, capacity models, and large-scale reliability improvements

Deep understanding of scheduling concepts including fairness, pre-emption, quota management, and multi-tenant isolation

Background in AI infrastructure, ML platforms, HPC, or large-scale batch and streaming systems

Hands-on experience with Kueue, Volcano, Ray, or similar Kubernetes-native orchestration frameworks