Staff Software Engineer, CAPE

What You'll Be Working On

• Building the Virtual Pool Service (VP Service), a physical infrastructure classification layer that serves as the single source of truth for every GPU node's state, pool membership, and transition history across Crusoe's fleet
• Designing and implementing Capacity Management Intelligence (CMI), the automation layer that handles priority-descending allocation, forward availability forecasting, and automated node lifecycle transitions — replacing manual spreadsheet workflows with enforced, auditable, event-driven automation
• Collaborating extensively across teams to architect and implement physical infrastructure management systems, availability platforms, and frameworks that meet end-to-end customer use cases
• Championing reliability, scalability, and security of our systems, designing high-performing, highly available cloud architectures optimized for both performance and cost-effectiveness
• Streamlining cloud deployment, configuration management, and operations using Go, gRPC, NATS event streaming, PostgreSQL (CNPG on Kubernetes), and Netbox as the physical source of truth
• Mentoring fellow engineers and actively contributing to team growth in collaboration with engineering managers

What You'll Bring to the Team

• A Bachelor's degree in Computer Science or Software Engineering
• 10+ years of relevant experience building and operating distributed systems at scale
• Proven experience building reliable, scalable, and secure cloud platforms and running them in production
• Strong distributed systems thinking with the ability to reason about consistency, failure modes, event ordering, and correctness invariants
• Fluency in Go, Rust, Java, or C++; Go is our primary language, but strong engineers from other backgrounds ramp quickly
• A collaborative, platform-minded approach to building robust systems and driving adoption across dev and ops teams
• Ownership mentality with comfort owning a system end to end: design, implementation, testing, ops, and iteration
• Good judgment under ambiguity, with the ability to drive open-ended technical decisions to resolution
• Excellent communication and troubleshooting skills across cross-functional teams

Bonus Points

• Hands-on experience deploying, managing, and troubleshooting Kubernetes clusters
• Prior experience with event-driven architectures or message streaming systems (NATS, Kafka, Kinesis)
• Experience with capacity planning, resource scheduling, or fleet management systems
• Background in GPU compute, AI/ML platform infrastructure, or fast-paced startup environments
• A passion for sustainability, clean energy, and building AI infrastructure that scales responsibly

Compensation

Compensation will be paid in the range of $209,000 - $253,000. Restricted Stock Units are included in all offers.