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. Ideal candidates are passionate about AI infrastructure, fluent in containerized environments, and confident in translating workloads across cloud platforms.

What You'll Be Working On

• 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: Go beyond abstracted services-deploy and optimize AI/ML workloads directly on Crusoe infrastructure. Ensure performance at the container and hardware level.
• Cross-Cloud Translation: Help customers migrate and adapt workloads across AWS, Azure, and GCP. Understand and explain the 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 that highlight real-world customer success.
• Voice of the Customer: Relay feedback to internal engineering and product teams to continuously improve Crusoe’s platform based on real-world implementation experience.

What You'll Bring to the Team

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

Bonus Points

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

Benefits

• Competitive compensation and equity packages
• Restricted Stock Units
• Paid time off, paid holidays & leave of absence programs
• Comprehensive health, dental & vision insurance
• Employer contributions to HSA account
• Paid parental leave
• Paid life insurance, short-term and long-term disability
• Professional development & tuition reimbursement
• Mental health & wellness support
• Commuter benefits (parking & transit)
• Cell phone stipend
• 401(k) Retirement plan with company match up to 4% of salary
• Volunteer time off
• Global travel insurance & emergency assistance
• Daily meals allowance
• Additional perks & programs specific to location

Compensation Range

Compensation will be paid in the range of up to $175,000 - $250,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicants knowledge, education, and abilities, as well as internal equity and alignment with market data.