Staff Product Manager (Compute)

Join Crusoe Cloud as a Staff Product Manager (Compute) and lead the strategy and execution of our core compute offerings. You will define how customers access and consume large-scale compute resources, translating evolving AI workload requirements into scalable, reliable, and economically efficient products. Collaborate closely with engineering, infrastructure operations, networking, storage, finance, sales, and customer success teams to deliver compute platforms that meet the needs of AI-native companies and enterprise customers running demanding workloads at scale.

• Defining and executing the product strategy for Crusoe Cloud compute services, including GPU and CPU infrastructure, bare metal offerings, and virtualized compute environments.
• Translating emerging AI workload requirements into product capabilities across performance, scheduling, isolation, utilization, and reliability.
• Collaborating closely with engineering, infrastructure operations, networking, storage, finance, sales, and customer success teams to deliver compute platforms that meet the needs of AI-native companies and enterprise customers.
• Strong communication skills with the ability to articulate technical concepts and connect them to customer and business outcomes
• Deep understanding of compute infrastructure concepts including virtualization, containerization, distributed systems, and large-scale cluster operations
• Strong product management experience delivering infrastructure or platform products, ideally within cloud infrastructure, HPC, or AI/ML environments
• Ability to balance technical tradeoffs, customer requirements, and infrastructure economics when making product decisions
• Experience working across hardware and software boundaries, translating infrastructure capabilities into customer-facing products
• Experience collaborating across engineering, operations, finance, and GTM teams to deliver complex infrastructure products
• Strong analytical skills with the ability to interpret utilization, performance, and financial metrics to guide roadmap prioritization
• Familiarity with GPU-based workloads and AI infrastructure, including training and inference characteristics, scheduling challenges, and performance considerations
• Background in infrastructure economics, capacity planning, or capital-intensive product environments
• Familiarity with Kubernetes, Slurm, or other workload orchestration systems used for AI and HPC workloads
• Experience working with large-scale GPU fleets or AI infrastructure platforms
• Experience building products for AI-native customers or operating AI workloads directly