Senior Staff Solutions Engineer (NYC)
About The Position
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.
Requirements
- 7+ years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi-node orchestration a must.
- Demonstrated success deploying ML frameworks (e.g., Ray, MLflow, Airflow) on Kubernetes—especially for inference and model training workflows.
- Familiarity with compute, storage, networking, and scaling in AWS, GCP, or Azure.
- Able to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in both pre- and post-sales environments.
- Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI.
- Strong communication skills and eagerness to partner cross-functionally with Engineering, Product, and Sales to make customers successful.
Nice To Haves
- 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)
- Experience translating workloads across clouds is highly desirable.
Responsibilities
- Lead technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers—owning the POC through to post-sales optimization.
- Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow) Design infrastructure that balances performance, scalability, and efficiency.
- Deploy and optimize AI/ML workloads directly on Crusoe infrastructure. Ensure performance at the container and hardware level.
- Help customers migrate and adapt workloads across AWS, Azure, and GCP. Understand and explain the tradeoffs between cloud-native and Crusoe-native approaches.
- Conduct workshops, live demos, and solution reviews. Contribute to case studies, solution briefs, and blog posts that highlight real-world customer success.
- Relay feedback to internal engineering and product teams to continuously improve Crusoe’s platform based on real-world implementation experience.
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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
