Prognostics & Health Monitoring Engineer

Responsibilities

• Define the vision, architecture, and roadmap for Prognostics and Health Monitoring (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 covering detection, diagnosis, action, and validation
• Influence hardware design, qualification, and operations through data-driven insights

Requirements

• Bachelor’s or Master’s in Engineering, Computer Science, Data Science, or a related field
• 8+ years of experience in reliability engineering, data science, fleet analytics, or a similar domain
• Strong proficiency in Python and SQL for large-scale data analysis and modeling
• Proven experience building and deploying predictive models in a production environment
• Expertise in applied statistics and probabilistic modeling (e.g., survival analysis, hazard models, Bayesian methods)
• Experience working with large-scale telemetry or distributed system datasets
• Ability to define ambiguous problems and deliver scalable solutions

Preferred Qualifications

• Experience with HPC systems, AI infrastructure, or datacenter environments
• Background in PHM, predictive maintenance, or reliability analytics at scale
• Familiarity with Remaining Useful Life (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

About the Company

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, empowering machine learning users to effortlessly run large-scale ML applications. Our technology is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via ultra high-speed inference.