Crusoe designs, builds, and operates energy-first AI infrastructure. Its offerings include Crusoe Cloud for GPU-based training, fine-tuning, inference, and managed services, alongside AI-optimized data centers and energy infrastructure.

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Skills

Ai Airflow Aws Azure Cli Cloud Infrastructure Customer Onboarding Docker Gcp Gpu Helm Inference Kubernetes Linux Machine-Learning Mlflow Mlops Model Training Multi Cloud Ray Slurm Technical Workshops Terraform

Candidate Availability

Onsite · Denver · Required

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

Lead technical onboarding and complex AI and machine learning deployments for strategic enterprise customers. Own proofs of concept through post-sale optimization, architect Kubernetes and MLOps infrastructure, optimize workloads at the container and hardware level, translate deployments across clouds, conduct workshops and demos, and communicate customer feedback to internal teams.

Requirements

7+ years building and deploying containerized workloads
Deep Kubernetes expertise
Experience with Helm, Terraform, Docker, and multi-node orchestration
Demonstrated success deploying Ray, MLflow, or Airflow on Kubernetes
Experience with inference and model training workflows
Knowledge of compute, storage, networking, and scaling in AWS, GCP, or Azure
Experience translating workloads across cloud platforms
Ability to lead technical engagements and gather requirements
Strong Linux and command-line proficiency
Experience supporting customers in pre-sales and post-sales environments
Experience with distributed ML orchestration platforms is a bonus
Exposure to Slurm is a bonus
Multi-cloud deployment or migration experience is a bonus

Responsibilities

Lead technical onboarding and deployment of complex AI and machine learning workloads
Own the proof-of-concept process through post-sale optimization
Architect and deploy ML workloads using Kubernetes-based stacks
Balance infrastructure performance, scalability, and efficiency
Deploy and optimize workloads at the container and hardware level
Help customers migrate and adapt workloads across AWS, Azure, and GCP
Conduct workshops, live demos, and solution reviews
Contribute to case studies, solution briefs, and blog posts
Relay customer feedback to engineering and product teams

Benefits

Restricted Stock Units
Paid time off
Paid holidays
Leave of absence programs
Comprehensive health insurance
Dental insurance
Vision insurance
Employer contributions to HSA account
Paid parental leave
Paid life insurance
Short-term disability insurance
Long-term disability insurance
Professional development
Tuition reimbursement
Mental health support
Wellness support
Commuter benefits for parking and 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 location-specific perks and programs