C
$155,000 – $200,000
Listed on Crusoe’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role involves helping enterprise customers successfully deploy AI and machine learning workloads on Crusoe's GPU infrastructure, moving from technical discovery through proof-of-concept to production launch. It's suited to cloud infrastructure engineers who want customer-facing technical responsibility and hands-on ownership of AI infrastructure challenges.
Our summary, not Crusoe’s wording. The full posting is on their site.
Skills this role names
- Amazon CloudWatch
- Amazon Web Services (AWS)
- Ansible
- AWS CloudFormation
- Bash (Scripting)
- Datadog
- Docker
- Grafana
- Kubeflow
- Kubernetes
- Linux
- Microsoft Azure
- Prometheus
- Python
- PyTorch
- Terraform
- V-Ray
Log in to see which of these are already on your profile.
What they ask for
Required
- 2+ years building, deploying, or operating cloud infrastructure
- Hands-on experience with at least one major cloud provider (AWS, GCP, or Azure)
- Kubernetes or Docker experience deploying containerized workloads
- Linux command-line proficiency and Python or Bash scripting
- Networking fundamentals (VPCs, subnets, load balancers, DNS, routing)
- Clear technical communication and demo presentation skills
- Customer-facing instincts and follow-through orientation
Nice to have
- Distributed training or inference frameworks experience (PyTorch, Ray, Kubeflow)
- Infrastructure-as-Code tools (Terraform, Ansible, CloudFormation)
- GPU cluster, InfiniBand/RoCE, or Slurm experience
- Monitoring and observability tooling (Prometheus, Grafana, Datadog, CloudWatch)
- Published technical content (talks, blog posts, guides)