Prognostics & Health Monitoring Engineer
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About the Role
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.
Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Role Summary
Quality, reliability, and uptime are foundational to scaling Cerebras systems. We are seeking an engineer to define and build our prognostics and health monitoring (PHM) capability—developing frameworks to monitor, assess, and predict hardware health across our fleet.
In this role, you will transform telemetry and operational data into actionable insights and automated responses, enabling early detection of degradation, accurate failure prediction, and proactive actions to keep systems highly available, performant, and resilient.
This is a highly cross-functional role spanning reliability engineering, data science, and system software, with broad influence across hardware, software, and fleet operations.
Responsibilities
- Define the vision, architecture, and roadmap for PHM across deployed systems
- Design and scale frameworks for health assessment, anomaly detection, and predictive failure modeling
- Develop and productionize probabilistic models for failure risk, degradation, and remaining useful life
- Analyze large-scale telemetry, logs, and service data to identify systemic drivers of failures and disruptions
- Establish health metrics, scoring systems, and fleet-level observability to communicate system risk
- Partner with system software to integrate monitoring, alerting, and automated mitigation into production
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