Staff Software Engineer

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.

What You'll Do:

The Platform & Infrastructure Engineering team sits at the core of our Data Infrastructure organization, responsible for the availability, reliability, scalability, and security of the company's data platform. We build and operate the foundational systems that underpin data ingestion, transformation, analytics, and internal AI workloads at scale — delivering with production-grade discipline across mission-critical services. Our work is guided by a commitment to automation, deep observability, and resilience, supporting stringent uptime requirements that the broader organization depends on.

About the role:

As a Staff Software Engineer, you will take end-to-end ownership of the reliability and performance of our Kubernetes-based data platform. You will architect and operate highly available, multi-region systems designed to meet rigorous uptime and latency standards. Your day-to-day work will span scaling infrastructure, optimizing deployment pipelines, and strengthening our security posture — while playing a central role in building, testing, and managing scalable systems that are production-ready from day one.

Who You Are:

• You bring 10+ years of hands-on experience in Platform Engineering, Infrastructure Engineering, or building highly scalable distributed systems — with a strong foundation in software design, development, and algorithmic problem-solving.

• Data Platform Experience: Familiarity operating data platforms or data-intensive workloads, including distributed processing and streaming frameworks such as Spark, Airflow, Kafka, or Flink.

• Kubernetes & Containerization: Deep expertise in Kubernetes cluster design, day-to-day operations, and production troubleshooting across containerized service environments.

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