Senior Software Engineer, AI Model Lifecycle
Crusoe is an AI infrastructure and cloud computing company. It provides GPU cloud capacity, managed AI services, inference, fine-tuning, data centers, and energy infrastructure for AI developers and enterprise customers.
Maintainer signals as of 8/23/2026
Funding history
Projects
About Crusoe, Inc
Crusoe designs, builds, and operates energy-first AI infrastructure, including data centers, GPU cloud computing, and modular AI factories. Crusoe Cloud provides GPU clusters, managed Kubernetes and Slurm, storage, networking, observability, managed inference, serverless fine-tuning, and model deployment through Crusoe Intelligence Foundry. Its customers include AI startups, enterprises, and organizations developing training, inference, analytics, and other compute-intensive workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Build a managed platform for the application development lifecycle focused on machine learning models and large language models. Manage fine-tuning systems, training pipelines, reinforcement learning workflows, and dataset, model, and experiment management at scale.
Requirements
- Advanced degree in Computer Science, Engineering, or a related field.
- 4-5+ years of industry experience leading impactful AI projects.
- Generative AI experience, including large language and multimodal models.
- Experience training, fine-tuning, and aligning large language models using reinforcement learning and reinforcement fine-tuning techniques.
- Ability to work autonomously and collaboratively.
- Proficiency in Golang or Python and PyTorch.
- Open-source AI project contributions.
- GPU performance optimization and inference framework experience.
Responsibilities
- Manage fine-tuning systems for large foundation models, including multi-node orchestration, checkpointing, failure recovery, and cost-efficient scaling.
- Implement and maintain end-to-end training pipelines for large language models.
- Apply reinforcement fine-tuning and reinforcement learning to fine-tuning and training workflows.
- Develop distillation and reinforcement learning pipelines for preference optimization, policy optimization, and reward modeling.
- Manage dataset, model, and experiment versioning, lineage, evaluation, and reproducible fine-tuning at scale.
Benefits
- Restricted Stock Units
- Paid time off
- Paid holidays
- Health insurance
- Dental insurance
- Vision insurance
- Employer contributions to HSA account
- Paid parental leave
- Paid life insurance
- Short-term disability insurance
- Long-term disability insurance
- Tuition reimbursement
- Mental health and wellness support
- Commuter benefits
- Cell phone stipend
- 401(k) retirement plan with company match up to 4% of salary
- Volunteer time off