Chief Architect - GPU Orchestration - Cloud Native - Startup - R

Chief Architect

• Startup (revenue-generating, Series A)
• Company size: 40
• REMOTE first culture
• Smart, fun, low-ego team culture
• Compensation: Base Salary 200k - 300k + Equity

Key Responsibilities:

• Extending capabilities beyond the current CPU optimization into GPU optimization.
• Develop capabilities to help customers rapidly set up ML Environment
• Architecture & Development: Kubernetes-based ML/AI platform.
• Leadership & Collaboration: with C-staff, product management, engineering, and design partners.
• Communication: Create detailed architecture diagrams, documents, and presentations.
• Focus on the User Experience (K8s users, Infrastructure Admin, MLOps staff, etc).
• Open Source Community: Stay actively involved with CNCF and related projects.
• Enterprise-Class Solutions: Drive & deliver solutions for enterprise-class data, ML, AI applications.
• FinOps & SRE Best Practices: FinOps for cloud financial management, modern SRE practices.

Qualifications:

• Entrepreneurial, Startup Experience
• 10 years+ infrastructure level software architecture and development.

Extensive Experience:

• Linux, Virtualization platforms (hands-on)
• AWS, GCP or Azure.

Strong experience:

• Kubernetes-based ML/AI systems (Kubeflow, Kueue, KServe, GPU Operators, DRA, Karpenter)

Deep knowledge:

• ML/AI use cases & customer stories of model development, training, inference, & hardware accelerator usage (CPU, GPU, TPU).
• Modern cloud-native architectures (scalability, availability, reliability, security, observability).
• Proven track record of delivering complex distributed systems.
• Active involvement in open-source communities, particularly CNCF and related projects.
• Strong leadership and team collaboration skills.
• Excellent communication skills, both verbal and written.

Preferred Qualifications:

• Knowledge of additional ML/AI frameworks and tools.
• Experience in DevOps practices and tools.
• Certification in Kubernetes or related technologies.
• Awareness of FinOps and SRE best practices
• Bachelors or Masters degree in Computer Science, Engineering, or related field.