Crusoe Energy Systems Logo

Crusoe Energy Systems

Staff Solutions Engineer

Reposted 13 Days Ago
Be an Early Applicant
Hybrid
San Francisco, CA
215K-250K Annually
Senior level
Hybrid
San Francisco, CA
215K-250K Annually
Senior level
The Staff Solutions Engineer will lead technical deployment of AI/ML workloads for enterprise customers, optimizing usage of Crusoe's AI infrastructure.
The summary above was generated by AI

Crusoe is building the World’s Favorite AI-first Cloud infrastructure company. We’re pioneering vertically integrated,  purpose-built AI infrastructure solutions trusted by Fortune 500 companies to power their most advanced AI applications. Crusoe is redefining AI cloud infrastructure, with a mission to align the future of computing with the future of the climate. Our AI platform is recognized as the "gold standard" for reliability and performance. Our data centers are optimized for AI workloads and are powered by clean, renewable energy.

Be part of the AI revolution with sustainable technology at Crusoe. Here, you'll drive meaningful innovation, make a tangible impact, and join a team that’s setting the pace for responsible, transformative cloud infrastructure.

About the Role:

Crusoe Cloud is seeking a Staff Solutions Engineer to work closely with our most strategic enterprise customers deploying AI/ML workloads on Crusoe’s high-performance GPU infrastructure. This is a hands-on, customer-facing role requiring deep technical expertise in Kubernetes, MLOps, and cloud infrastructure.

You’ll guide customers through end-to-end deployment—owning the PoC process, optimizing workloads post-sale, and serving as a critical technical voice between our customers and engineering teams. Ideal candidates are passionate about AI infrastructure, fluent in containerized environments, and confident translating workloads across cloud platforms.

What You'll Be Working On:

  • Customer Enablement: Lead technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers—owning the PoC through to post-sales optimization.

  • Kubernetes + MLOps Focus: Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow) Design infrastructure that balances performance, scalability, and efficiency.

  • Infrastructure-Centric Thinking: Go beyond abstracted services—deploy and optimize AI/ML workloads directly on Crusoe infrastructure. Ensure performance at the container and hardware level.

  • Cross-Cloud Translation: Help customers migrate and adapt workloads across AWS, Azure, and GCP. Understand and explain the tradeoffs between cloud-native and Crusoe-native approaches.

  • Technical Storytelling: Conduct workshops, live demos, and solution reviews. Contribute to case studies, solution briefs, and blog posts that highlight real-world customer success.

  • Voice of the Customer: Relay feedback to internal engineering and product teams to continuously improve Crusoe’s platform based on real-world implementation experience.

What You'll Bring to the Team:

  • Deep Kubernetes Expertise: 5-7 years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi-node orchestration a must.

  • MLOps Deployment Experience: Demonstrated success deploying ML frameworks (e.g., Ray, MLflow, Airflow) on Kubernetes—especially for inference and model training workflows.

  • Hands-on Cloud Infrastructure Knowledge:Familiarity with compute, storage, networking, and scaling in AWS, GCP, or Azure. Experience translating workloads across clouds is highly desirable.

  • Customer-Facing Technical Confidence: Able to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in both pre- and post-sales environments.

  • Strong Linux and CLI Proficiency:Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI.

  • Collaborative Energy: Strong communication skills and eagerness to partner cross-functionally with Engineering, Product, and Sales to make customers successful.

Bonus Points

  • Experience with Ray, Kubeflow, or other distributed ML orchestration platforms

  • Exposure to Slurm, but with a primary focus on containerized MLOps over traditional HPC

  • Multi-cloud deployment or migration experience (especially AWS ➝ Crusoe transitions)

  • Content contributions (tech talks, blogs, public case studies

Benefits:

  • Hybrid work schedule

  • Industry competitive pay

  • Restricted Stock Units in a fast growing, well-funded technology company

  • Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents

  • Employer contributions to HSA accounts 

  • Paid Parental Leave 

  • Paid life insurance, short-term and long-term disability 

  • Teladoc 

  • 401(k) with a 100% match up to 4% of salary

  • Generous paid time off and holiday schedule

  • Cell phone reimbursement

  • Tuition reimbursement

  • Subscription to the Calm app

  • MetLife Legal

  • Company paid commuter benefit; $200/month

Compensation Range

Compensation will be paid in the range of up to $215,000 -$250,000 per year + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicants 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.

Top Skills

Airflow
AWS
Azure
Docker
GCP
Helm
Kubeflow
Kubernetes
Linux
Mlflow
Mlops
Ray
Terraform

Crusoe Energy Systems San Francisco, California, USA Office

San Francisco, CA, United States

Similar Jobs

2 Days Ago
In-Office or Remote
4 Locations
136K-214K Annually
Mid level
136K-214K Annually
Mid level
Artificial Intelligence • Machine Learning
Lead pre-sales technical evaluations, advise on AI strategies, and assist with AI development while collaborating with sales and technical teams.
Top Skills: Cloud PlatformsHuggingfaceLlamaMistralMlopsOpenaiPythonPyTorch
4 Days Ago
In-Office
10 Locations
115K-230K Annually
Senior level
115K-230K Annually
Senior level
Insurance
As a Staff Engineer, lead engineering teams, own product development, mentor juniors, and ensure high standards in tech solutions while collaborating with cross-functional teams.
Top Skills: .NetAzureC++DockerJavaKubernetesMicro-ServicesNoSQLPowershellPythonRest ApisSQL
7 Days Ago
In-Office
Santa Clara, CA, USA
100K-150K
Senior level
100K-150K
Senior level
Cybersecurity
Lead the design, development, and implementation of AI solutions across various enterprise functions, mentoring engineers and ensuring responsible AI practices.
Top Skills: Aws SagemakerAzure MlGoGoogle Cloud AiJavaPythonPyTorchTensorFlow

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account