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Job Description
Crusoe's mission is to accelerate the abundance of energy and intelligence. We’re crafting the engine that powers a world where people can create ambitiously with AI — without sacrificing scale, speed, or sustainability.
Be part of the AI revolution with sustainable technology at Crusoe. Here, you'll drive meaningful innovation, make a tangible impact, and join a team that’s setting the pace for responsible, transformative cloud infrastructure.
About the Role:
Crusoe Cloud is seeking a Sr. 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 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: Deploy and optimize AI/ML workloads directly on Crusoe infrastructure, ensuring performance at the container and hardware level.
Cross-Cloud Translation: Assist customers in migrating and adapting workloads across AWS, Azure, and GCP. Understand and explain the tradeoffs between cloud-based and Crusoe approaches.
Technical Storytelling: Conduct workshops, live demos, and solution reviews. Contribute to case studies, solution briefs, and blog posts showcasing real-world customer success.
Voice of the Customer: Relay feedback to internal engineering and product teams to improve Crusoe’s platform based on real-world implementation.
What You'll Bring to the Team: Deep Kubernetes Expertise: 3-5 years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi-node orchestration is required.
MLOps Deployment Experience: Proven success deploying ML frameworks (e.g., Ray, MLflow, Airflow) on Kubernetes, especially for inference and training workflows.
Hands-on Cloud Infrastructure Knowledge: Familiarity with compute, storage, networking, and scaling in AWS, GCP, or Azure. Experience with multi-cloud workload migration is highly desirable.
Customer-Facing Technical Confidence: Ability to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers pre- and post-sales.
Strong Linux and CLI Proficiency: Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI.
Collaborative Energy: Excellent communication skills and eagerness to work cross-functionally with Engineering, Product, and Sales teams to ensure customer success.
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 such as tech talks, blogs, or public case studies.
Benefits: Industry-competitive pay
Restricted Stock Units in a fast-growing, well-funded tech company
Comprehensive health insurance options including HDHP, PPO, vision, and dental for employees and dependents
Employer contributions to HSA accounts
Paid parental leave, life insurance, short- and long-term disability
Teladoc access
401(k) plan with 100% match up to 4%
Generous paid time off and holidays
Cell phone reimbursement
Tuition reimbursement
Subscription to Calm app
MetLife Legal services
Company-paid commuter benefit ($200/month)
Compensation Range: Up to $157,000 - $205,000 per year plus bonus. Restricted Stock Units are included. Compensation will be based on experience, education, abilities, and market considerations. Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected class.
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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: Assist customers in migrating and adapting workloads across AWS, Azure, and GCP. Understand and explain the tradeoffs between cloud-based and Crusoe approaches.
Technical Storytelling: Conduct workshops, live demos, and solution reviews. Contribute to case studies, solution briefs, and blog posts showcasing real-world customer success.
Voice of the Customer: Relay feedback to internal engineering and product teams to improve Crusoe’s platform based on real-world implementation.
What You'll Bring to the Team: Deep Kubernetes Expertise: 3-5 years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi-node orchestration is required.
MLOps Deployment Experience: Proven success deploying ML frameworks (e.g., Ray, MLflow, Airflow) on Kubernetes, especially for inference and training workflows.
Hands-on Cloud Infrastructure Knowledge: Familiarity with compute, storage, networking, and scaling in AWS, GCP, or Azure. Experience with multi-cloud workload migration is highly desirable.
Customer-Facing Technical Confidence: Ability to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers pre- and post-sales.
Strong Linux and CLI Proficiency: Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI.
Collaborative Energy: Excellent communication skills and eagerness to work cross-functionally with Engineering, Product, and Sales teams to ensure customer success.
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 such as tech talks, blogs, or public case studies.
Benefits: Industry-competitive pay
Restricted Stock Units in a fast-growing, well-funded tech company
Comprehensive health insurance options including HDHP, PPO, vision, and dental for employees and dependents
Employer contributions to HSA accounts
Paid parental leave, life insurance, short- and long-term disability
Teladoc access
401(k) plan with 100% match up to 4%
Generous paid time off and holidays
Cell phone reimbursement
Tuition reimbursement
Subscription to Calm app
MetLife Legal services
Company-paid commuter benefit ($200/month)
Compensation Range: Up to $157,000 - $205,000 per year plus bonus. Restricted Stock Units are included. Compensation will be based on experience, education, abilities, and market considerations. Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected class.
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