Prognostics & Health Monitoring Engineer

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 

• Drive closed-loop systems (detection → diagnosis → action → validation) 

• Influence hardware design, qualification, and operations through data-driven insights 

Skills & Qualifications 

Required: 

• Bachelor’s or Master’s in Engineering, Computer Science, Data Science, or related field 

• 8+ years in reliability engineering, data science, fleet analytics, or similar 

• Strong Python and SQL for large-scale data analysis and modeling 

• Experience building and deploying predictive models in production 

• Expertise in applied statistics and probabilistic modeling (e.g., survival analysis, hazard models, Bayesian methods) 

• Experience with large-scale telemetry or distributed system datasets 

• Proven ability to define ambiguous problems and deliver scalable solutions 

Preferred: 

• Experience with HPC systems, AI infrastructure, or datacenter environments 

• Background in PHM, predictive maintenance, or reliability analytics at scale 

• Familiarity with RUL estimation and degradation modeling 

• Understanding of observability systems, telemetry pipelines, and real-time monitoring 

• Background in hardware reliability and failure modes in complex systems 

 

The base salary range for this position is $150,000 to $250,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.