Senior Platform Engineer
Hyperbolic Labs6 months ago
Responsibilities
- Design and build the control plane that aggregates compute from diverse GPU vendors into a unified platform.
- Architect tenant management, resource lifecycle APIs, identity and access control, billing, quota, and multi-cloud abstraction systems.
- Create policies, compliance hooks, governance frameworks, and golden paths for developers.
- Develop robust, scalable, maintainable, and developer-friendly distributed platform services.
- Collaborate with product, engineering, and business teams on foundational platform architecture.
Requirements
- Expertise designing and building control planes for cloud, developer, or infrastructure platforms.
- Strong API design and development experience, including resource lifecycles, versioning strategies, and multi-cloud abstractions.
- Deep understanding of multi-tenancy, tenant isolation, project hierarchies, resource quotas, and billing integration.
- Experience with identity and access management, SSO integration, organizational structures, RBAC, and fine-grained permissions.
- Ability to design developer-friendly golden paths and scalable distributed control-plane architectures.
- Experience building compliance hooks, policy enforcement, governance frameworks, and production-grade platform services.
- Excellent communication and cross-functional collaboration skills.
- Preferred experience with cloud platforms, PaaS, or infrastructure-as-a-service products.
- Preferred knowledge of Kubernetes control plane architecture, custom resource definitions, and operator patterns.
- Preferred familiarity with billing systems, metering, usage tracking, quota enforcement, service mesh, API gateways, and rate limiting.
- Preferred background in security, SOC 2, GDPR, HIPAA, audit logging, and open-source platform or infrastructure projects.
Tech Stack
About Hyperbolic Labs
Hyperbolic Labs builds an open-access AI cloud that provides on-demand GPU clusters, a GPU marketplace for idle compute, and managed inference for AI startups, ML teams, and researchers. It sells capacity and services for training and serving models at production scale. The company is privately held, headquartered in San Francisco, and raised a Series A in 2024.