Senior Director of AI Model LifeCycle, Crusoe Energy Systems

• Advanced degree in Computer Science, Engineering, or a related field
• 10+ years of industry experience leading and driving impactful projects in the AI Space
• Lead and mentor a team of engineers with exceptional interpersonal skills, working autonomously while proactively collaborating with stakeholders at all levels
• Experience in Generative AI (Large Language Models, Multimodal)
• Hands-on experience training, fine-tuning, and aligning LLMs using Reinforcement Learning and Reinforcement Fine-Tuning (RFT) techniques
• Proactive and collaborative approach with the ability to work autonomously
• Passion for building cutting-edge AI products and solving challenging technical problems
• 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

Desirable

• 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)
• 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