Senior Staff Solutions Engineer (NYC)

About the Role

Crusoe Cloud is seeking a Sr. to Senior Staff level Solutions Engineer to work closely with our most strategic enterprise customers deploying AI/ML workloads on Crusoe’s high-performance GPU infrastructure. This is a hands-on, customer-facing role requiring deep technical expertise in Kubernetes, MLOps, and cloud infrastructure.

You’ll guide customers through end-to-end deployment—owning the PoC process, optimizing workloads post-sale, and serving as a critical technical voice between our customers and engineering teams.

Responsibilities

• Customer Enablement: Lead technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers—owning the POC through to post-sales optimization.
• Kubernetes + MLOps Focus: Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow). Design infrastructure that balances performance, scalability, and efficiency.
• Infrastructure-Centric Thinking: Deploy and optimize AI/ML workloads directly on Crusoe infrastructure, ensuring performance at the container and hardware level.
• Cross-Cloud Translation: Help customers migrate and adapt workloads across AWS, Azure, and GCP. Explain tradeoffs between cloud-native and Crusoe-native approaches.
• Technical Storytelling: Conduct workshops, live demos, and solution reviews. Contribute to case studies, solution briefs, and blog posts.
• Voice of the Customer: Relay feedback to internal engineering and product teams to continuously improve Crusoe’s platform.

Requirements

• Kubernetes Expertise: 7+ years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi-node orchestration.
• MLOps Deployment Experience: Demonstrated success deploying ML frameworks (e.g., Ray, MLflow, Airflow) on Kubernetes—especially for inference and model training workflows.
• Cloud Infrastructure Knowledge: Familiarity with compute, storage, networking, and scaling in AWS, GCP, or Azure.
• Customer-Facing Confidence: Ability to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in pre- and post-sales environments.
• Linux & CLI Proficiency: Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI.
• Collaborative Energy: Strong communication skills and cross-functional collaboration with Engineering, Product, and Sales.

Bonus Points

• Experience with Ray, Kubeflow, or other distributed ML orchestration platforms
• Exposure to Slurm (with a primary focus on containerized MLOps over traditional HPC)
• Multi-cloud deployment or migration experience (especially AWS to Crusoe transitions)
• Content contributions (tech talks, blogs, public case studies)