Engineering Manager, Crusoe Energy Systems

• 5+ years managing/leading high-performing engineering teams
• Ability to lead teams through ambiguity and align on complex technical goals
• Proven success hiring, developing, and retaining talent
• Hands-on experience with distributed and concurrent systems or AI infrastructure
• Deep knowledge of cloud-native environments, container orchestration, and SOAs
• Some familiarity with CPU & GPU performance, inference frameworks, or LLM systems is a strong plus
• Comfortable owning deliverables from design through production
• Strong collaboration skills, prioritizing clarity, context, and customer impact
• Experience in fast-paced startup or growth-stage environments
• Growth-minded leader who leads and empowers others
• Excellent communicator and relationship builder
• Passionate about building world-class AI infrastructure and teams

Desirable

• As an Engineering Manager on the Managed AI team at Crusoe, you will play a critical role in leading and scaling a team of engineers building our next-generation platform for the full lifecycle of Large Language Models (LLMs). You will be responsible for guiding the team through the design and implementation of highly scalable, fault-tolerant infrastructure
• You will lead a team of high-caliber engineers, combining technical expertise with strong people leadership. This role is central to a fast-growing, strategically important organization, where you will shape the engineering roadmap and drive the execution of key projects. Success requires close partnership with product, business, and platform stakeholders to deliver a performant and reliable platform that powers AI for customers globally
• Lead, mentor, and grow a team of high-caliber software engineers on Crusoe’s
• Partner with leadership to define and execute the AI roadmap, setting clear goals and driving accountability
• Cultivate a high-performance, collaborative engineering culture grounded in technical excellence
• Oversee the architecture and development of core AI services: fault-tolerant task queues, model management systems, cost-aware scheduling, etc
• Ensure delivery of scalable systems capable of handling millions of API requests per second
• Deliver an AI platform that can handle a large variety of load from training, to agentic execution infrastructure
• Work cross-functionally with Product, Infrastructure, and GTM stakeholders
• Represent Engineering in strategic discussions to influence AI platform growth and customer adoption
• Promote knowledge sharing, technical mentorship, and the evolution of engineering processes