Job description

Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe. About the Role: 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. This role is based full-time (Monday through Friday) in our New York City office. 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. Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.

Requirements

Must-haves

If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.
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
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

Nice-to-haves

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

Application prep

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Keywords to mirror

aimlkubernetesawsgcpazuredockerpython

Platform: ashby
Effort: quick

Benefits

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Dental

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401(K)

Paid Time Off

Parental Leave

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Professional Development