Job Opportunities API

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 .   We're proud to be a Living Wage accredited Employer.   What You'll Do: CoreWeave’s Inference team builds and operates the core cloud platform powering massive-scale GPU workloads for AI/ML, VFX, rendering, and real-time inference. Our stack is engineered for speed, scale, and cost efficiency—providing a powerful alternative to traditional hyperscalers. Infrastructure is our product, and we operate it at massive scale to power the most demanding workloads. About the role:   As a Staff Software Engineer (IC5) on the Inference team, you will operate as a technical leader across multiple teams and services, driving architecture, performance, and reliability for CoreWeave’s Kubernetes-native inference platform. You will define and lead complex, cross-cutting design initiatives spanning request routing, adaptive scheduling, GPU resource management, and cost-per-token optimization under strict P99 SLAs. This high-impact role requires you to implement advanced inference optimizations—such as speculative decoding and KV-cache reuse—while establishing performance benchmarking frameworks, guiding cross-functional alignment across infrastructure boundaries, and raising the bar for engineering rigor and observability practices. Who You Are: ~8–12+ years of experience building large-scale distributed systems or cloud platforms. Proven track record of leading cross-team or organization-level technical initiatives at scale. Strong coding skills in Go, Python, or C++. Deep expertise in Kubernetes at production scale, including orchestration, scheduling, and service design. Strong understanding of networked systems, performance optimization, and distributed system design. Hands-on engineering experience with inference systems, including batching/micro-batching strategies, caching, memory optimization, mixed precision (BF16/FP8), and streaming token delivery. Demonstrated ability to systematically improve tail latency (P95/P99) and platform reliability through metrics-driven engineering. Experienced in owning system-wide SLIs/SLOs, capacity planning, autoscaling strategies, and mentoring senior and mid-level engineers. Bachelor’s degree in Computer Science, Engineering, or a related technical field. Preferred: Direct open-source or production contributions to modern inference frameworks such as vLLM, Triton, TensorRT-LLM, Ray Serve, or TorchServe. Deep experience with GPU systems engineering and hardware performance optimization (CUDA, NCCL, RDMA, NUMA, or GPU interconnects). Direct exposure to large-scale AI/ML infrastructure or hyperscale cloud environments. Wondering if you're a good fit?   We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams even if you aren't a 100% skill or experience match. Here are a few qualities we've found compatible with our team. If some of this describes you, we'd love to talk. You love to scale highly complex distributed architectures and mentor engineering cohorts to elevate technical standards across an organization. You're curious about pioneering low-latency inference optimizations and finding innovative shortcuts to optimize cost-per-token performance. You're an expert in metrics-driven engineering, troubleshooting micro-bottlenecks, and delivering stable cloud platforms under strict multi-tena