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CrusoeCrusoeNew York, NY

Senior Solutions Engineer

Solutions Engineer helping enterprise customers deploy and optimize AI/ML workloads on Crusoe's GPU cloud infrastructure. Conduct technical discovery, deliver demos and PoCs, troubleshoot containerized workloads, and support migration from major cloud providers.

155k – 200k/yr
On-site5+ YOESolutions Architecture

About the role

What You'll Be Working On

  • 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.

What You'll Bring to the Team

  • 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.

Bonus Points

  • 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.

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.

Compensation Range

Compensation will be paid in the range of up to $155,000 - $200,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant's knowledge, education, and abilities, as well as internal equity and alignment with market data.

Skills

KubernetesDockerAWSGCPAzureLinuxPythonBashTerraformPyTorchPrometheusGrafana
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