Solutions Engineer
Crusoe is an AI infrastructure company that designs, builds, and operates AI data centers and a cloud platform. It provides managed AI services, GPU compute, model fine-tuning and inference, and infrastructure operations for organizations building and deploying AI workloads.
Maintainer signals as of 8/14/2026
Funding history
Projects
About Crusoe
Crusoe, the AI factory company, provides Crusoe Cloud and Crusoe Intelligence Foundry for AI development and production. Its offerings include managed inference, serverless fine-tuning, high-performance NVIDIA and AMD compute, accelerated storage, RDMA networking, managed Kubernetes and Slurm, and operations tooling. The company also designs, builds, and operates modular AI data-center infrastructure using an energy-first approach, serving customers that need scalable training, inference, and AI platform infrastructure.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead technical onboarding and deployment of complex AI and machine learning workloads for strategic enterprise customers. You will own proofs of concept through post-sale optimization, architect Kubernetes and MLOps infrastructure, optimize workloads, translate deployments across cloud platforms, conduct workshops and demos, and relay customer feedback to engineering and product teams.
Requirements
- 3-5 years building and deploying containerized workloads
- Experience with Helm, Terraform, Docker, and multi-node orchestration
- Demonstrated success deploying Ray, MLflow, or Airflow on Kubernetes
- Experience with inference and model training workflows
- Knowledge of compute, storage, networking, and scaling in AWS, GCP, or Azure
- Experience translating workloads across cloud platforms
- Ability to navigate stakeholder conversations, gather requirements, and lead technical engagements
- Strong Linux and command-line proficiency
- Ability to troubleshoot infrastructure issues via CLI
- Experience with distributed ML orchestration platforms is a bonus
- Exposure to Slurm is a bonus
- Multi-cloud deployment or migration experience is a bonus
- Ability to pass a background check
Responsibilities
- Lead technical onboarding and deployment of complex AI and machine learning workloads
- Own the proof-of-concept process through post-sale optimization
- Architect and deploy ML workloads using Kubernetes-based stacks
- Balance infrastructure performance, scalability, and efficiency
- Deploy and optimize workloads directly on infrastructure
- Help customers migrate and adapt workloads across AWS, Azure, and GCP
- Conduct workshops, live demos, and solution reviews
- Contribute to case studies, solution briefs, and blog posts
- Relay customer feedback to engineering and product teams
Benefits
- Pension contributions
- Private health insurance
- Dental insurance
- Income protection
- Life assurance