About the job
Crusoe Cloud is seeking a Solutions Engineer (early-to-mid career through senior level) to help enterprise customers deploy AI/ML workloads on Crusoe's high-performance GPU infrastructure. Based in our New York City office, you'll work alongside Account Executives and senior Solutions Engineers to run technical discovery, deliver demos and proofs of concept, and ensure customers land successfully on the platform. This is a hands-on role where you'll build environments, troubleshoot workloads, and grow toward owning the technical win end-to-end.
Responsibilities
- Deliver technical demos and stand up PoC environments for customers evaluating Crusoe Cloud, with clearly defined success criteria and timely execution
- Support technical discovery alongside Account Executives, including mapping stakeholders, gathering requirements, and handling technical objections
- Deploy and troubleshoot containerized AI/ML workloads on Kubernetes-based stacks, optimizing for performance and cost
- Help customers adapt workloads from AWS, GCP, or Azure to Crusoe infrastructure, explaining tradeoffs along the way
- Document product gaps and bugs from the field with the detail Engineering needs to reproduce them, and channel structured customer feedback to Product
- Ensure clean post-sale handoffs by building transition docs and instance summaries so customer success teams start from a stable Day 1
Qualifications
Minimum
- 2+ years of hands-on experience building, deploying, or operating cloud infrastructure, with exposure to AI/ML, HPC, or GPU workloads
- Hands-on proficiency with at least one major cloud provider (AWS, GCP, or Azure), deploying and managing infrastructure rather than just consuming it
- Experience deploying containerized workloads with Kubernetes or Docker
- Strong Linux command-line skills and scripting ability in Python or Bash
- Networking fundamentals: VPCs, subnets, load balancers, DNS, routing
- Clear technical communication, comfortable presenting demos and writing documentation customers actually use
- Customer-facing instincts: curiosity about business problems, composure under questions, and a bias toward follow-through
Preferred
- Experience with distributed training or inference frameworks (PyTorch, Ray, Kubeflow)
- Infrastructure-as-Code experience (Terraform, Ansible, CloudFormation)
- GPU cluster, InfiniBand/RoCE, or Slurm exposure
- Monitoring and observability tooling (Prometheus, Grafana, Datadog, CloudWatch)
- Public technical content such as talks, blog posts, or how-to guides