About the role
Crusoe is on a mission to accelerate the abundance of energy and intelligence . As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe. About This Role: The Senior Director for the Model LifeCycle team will undertake a pivotal role in establishing both a dedicated team and a comprehensive managed platform to oversee the entire application development lifecycle, with a particular emphasis on utilizing Machine Learning models, including Large Language Models (LLMs). What You’ll Be Working On: Building a Team of Machine Learning Experts and being the Site leader for the Model Life Cycle Team. Manage fine-tuning systems for large foundation models (SFT, PEFT, LoRA, adapters), including multi-node orchestration, checkpointing, failure recovery, and cost-efficient scaling. Implement and maintain end-to-end training pipelines for Large Language Models. RFT and Reinforcement learning to the fine tuning and training sections Distillation and reinforcement learning pipelines (e.g., preference optimization, policy optimization, reward modeling). Dataset, model, and experiment management: versioning, lineage, evaluation, and reproducible fine-tuning at scale. What You’ll Bring to