The opportunity
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
What you'll do
Define and drive reliability strategy: establish SLOs and ensure alignment across engineering.
Design and implement reliability mechanisms: build and evolve systems for fault detection, graceful degradation, failover, throttling, and recovery across multiple regions and data centers.
Lead large-scale incident management: own postmortems, root-cause analysis, and prevention loops for reliability-related incidents.
Architect for reliability and observability: influence system design for redundancy, durability, and debuggability.
Develop reliability tooling: create internal tools and frameworks for chaos testing, load simulation, and distributed fault injection.
Collaborate broadly: work across software, infrastructure, and hardware teams to ensure reliability is embedded into every layer of our inference service.
What they're looking for
- Monitor and communicate reliability metrics: build dashboards and alerts that measure service health and provide actionable insights.
- Mentor and influence: guide engineers and set best practices for designing, testing, and operating reliable large-scale systems.
- Bachelor's or master's degree in computer science or related field.
- + years of experience in backend, infrastructure, or reliability engineering: for large-scale distributed systems.
