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Senior to Senior Staff Solutions Engineer

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Crusoe

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Location:
United States , San Francisco ▼

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

175000.00 - 250000.00 USD / Year
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Job Description:

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 translating workloads across cloud platforms.

Job Responsibility:

  • 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

Requirements:

  • Deep 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
  • 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

Nice to have:

  • 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)
What we offer:
  • Restricted Stock Units in a fast growing, well-funded technology company
  • Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents
  • Employer contributions to HSA accounts
  • Paid Parental Leave
  • Paid life insurance, short-term and long-term disability
  • Teladoc
  • 401(k) with a 100% match up to 4% of salary
  • Generous paid time off and holiday schedule
  • Cell phone reimbursement
  • Tuition reimbursement
  • Subscription to the Calm app
  • MetLife Legal
  • Company paid commuter benefit
  • $300/month

Additional Information:

Job Posted:
February 21, 2026

Employment Type:
Fulltime
Work Type:
On-site work
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