Staff Inference Software Engineer, CoreWeave

• This role requires hands-on technical leadership and the ability to influence engineering direction across the organization
• 8–12+ years of experience building and operating large-scale distributed systems or cloud platforms
• Proven experience leading cross-team technical initiatives impacting multiple services or organizations
• Strong programming skills in Go, Python, or C++
• Deep expertise in Kubernetes at production scale, including orchestration, scheduling, and service design
• Strong understanding of distributed systems, networking, and performance optimization
• Experience designing and operating low-latency, high-throughput systems with strict P95/P99 latency requirements
• Hands-on experience with inference systems, including batching or micro-batching strategies, caching, and memory optimization
• Experience improving system performance using metrics-driven approaches (e.g., latency, throughput, utilization)
• Familiarity with mixed precision (BF16, FP8) and streaming inference workloads
• 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 design and optimize high-performance distributed systems at scale
• You’re curious about AI inference, GPU systems, and emerging performance techniques
• You’re an expert in building reliable, low-latency infrastructure and driving system-wide improvements

Desirable

• The Inference team builds and operates CoreWeave’s Kubernetes-native inference platform, powering low-latency, high-throughput AI workloads at massive scale
• The team is responsible for request routing, scheduling, GPU resource management, and system-wide optimizations that drive performance, efficiency, and reliability across real-time inference systems
• As a Staff Software Engineer (IC5) on the Inference team, you will act as a technical leader driving architecture, performance, and reliability across multiple services and teams
• Your day-to-day will involve leading cross-team design initiatives, optimizing inference performance (latency, throughput, and GPU utilization), and improving system reliability at scale
• You will work deeply in distributed systems and Kubernetes-based infrastructure, focusing on areas like scheduling, batching, and memory optimization