Engineering Manager, Crusoe Energy Systems

• 2+ years managing or technically leading high-performing engineering teams
• Proven track record of delivering performance-critical software in AI, HPC, or similar environments
• Strong ability to prioritize work, communicate trade-offs, and lead teams through ambiguity
• Strong understanding of CUDA or OpenCL with experience in kernel development or GPU optimization
• Proficiency in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow
• Expertise in CPU/GPU performance profiling and system-level optimization

Desirable

• Crusoe Energy is on a mission to align the future of computing with the future of the climate
• As an Engineering Manager, AI Performance, you will lead a team of high-performance computing experts focused on optimizing Crusoe’s inference engines and AI infrastructure
• Your team’s work will directly impact Crusoe’s core business by accelerating model throughput and maximizing hardware efficiency
• In this hybrid leadership role, you will combine technical expertise with people management to deliver performance gains across our scalable AI systems
• You’ll partner closely with AI researchers, infrastructure engineers, and product leaders to push the boundaries of what’s possible in real-time inference
• Manage and grow a team of performance engineers optimizing Crusoe’s inference stack
• Define team goals, set technical direction, and ensure timely delivery of high-impact projects
• Provide coaching, mentorship, and support for individual team member development
• Guide performance improvements in inference engines such as VLLM and related systems
• Lead efforts to implement optimizations that increase token throughput and system efficiency
• Oversee the development of CUDA kernels and other low-level performance enhancements for deep learning workloads
• Partner with AI researchers, product managers, and infrastructure teams to prioritize and deliver performance improvements
• Drive initiatives to improve observability, performance profiling, and benchmarking infrastructure
• Contribute to internal standards for onboarding, documentation, and knowledge sharing across the org
• Stay informed on the latest trends in high-performance AI infrastructure and inference acceleration
• Support contributions to open-source projects and participate in relevant research communities and forums