What are the responsibilities and job description for the Hyperbolic Labs - Senior Site Reliability Engineer position at deCircle?
Hyperbolic Labs is on a mission to democratize AI by breaking down the barriers to computing power with our Open-Access AI Cloud. By aggregating computing resources across the globe, we offer an innovative GPU marketplace and AI inference service that promise affordability and accessibility for all. As pioneers at the intersection of AI and open-source technology, we believe in an open future where AI innovation is limited only by imagination, not by access to resources. We're looking for forward-thinking individuals who share our passion for making AI universally accessible, secure, and affordable. Join us in building a platform that empowers innovators everywhere to turn their visionary AI projects into reality.
As we prepare for growth after our Series A, our team — led by co-founders with PhDs in AI, Math, and Computer Science — is poised to redefine computing.
About the Role
We're seeking a Site Reliability Engineer to ensure Hyperbolic's GPU marketplace and AI infrastructure operate with exceptional reliability, performance, and security. As an aggregator of compute resources from hundreds of global suppliers, our SLOs, trust, and economic efficiency are product-critical. You'll be responsible for defining and maintaining service level objectives for job success rates, building robust incident response systems, managing capacity across our distributed GPU network, and implementing secure rollout and rollback mechanisms that keep our platform running smoothly 24/7.
In this role, you'll establish the reliability standards that define customer trust in our platform, design monitoring and alerting systems that provide deep visibility into our infrastructure, build automation for capacity management and resource allocation, lead incident response and post-mortem processes, and work closely with engineering teams to improve system resilience. You'll also focus on security and infrastructure hardening, ensuring strong isolation between tenants and suppliers, implementing key management systems, and building compliance frameworks. This is a high-impact position where your work directly influences our ability to deliver on our promise of affordable, accessible AI compute at scale.
Expert in site reliability engineering with proven experience defining, monitoring, and maintaining SLOs and SLAs for production systems
Strong background in capacity planning and management, including forecasting, resource allocation, and cost optimization for distributed systems
Experienced in incident response, on-call rotations, and post-mortem processes with a track record of reducing MTTR and improving system resilience
Deep knowledge of deployment systems including progressive rollouts, canary deployments, feature flags, and automated rollback mechanisms
Proficient in observability tools and practices including metrics, logging, tracing, and alerting systems (Prometheus, Grafana, ELK stack, or similar)
Strong understanding of infrastructure security including tenant isolation, workload isolation, network segmentation, and security hardening
Experience with secrets management, key management systems (KMS), certificate management, and secure credential rotation
Knowledge of compliance frameworks and security best practices for cloud platforms (SOC 2, ISO 27001, or similar)
Excellent problem-solving skills with ability to debug complex distributed systems issues under pressure
Strong automation mindset with experience using infrastructure-as-code, configuration management, and CI/CD pipelines
Preferred Qualifications
Experience operating GPU infrastructure, AI/ML platforms, or compute marketplaces at scale
Background in distributed systems, peer-to-peer networks, or decentralized infrastructure
Knowledge of multi-tenancy security patterns, container security, and runtime security tools
Experience with chaos engineering, fault injection, and resilience testing
Familiarity with cost optimization strategies for cloud infrastructure and GPU resources
Experience building and operating systems with demanding uptime requirements (99.9% SLAs)
Background at companies like AWS, Google Cloud, Azure, or fast-growing infrastructure startups
Contributions to open-source reliability, observability, or security tools