Prognostics & Health Monitoring Engineer

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

Requirements

• 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.
• Why Join Cerebras
• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source their cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Our simple, non-corporate work culture that respects individual beliefs.
• Read our blog: Five Reasons to Join Cerebras in 2026.
• Apply today and become part of the forefront of groundbreaking advancements in AI!

Benefits

Additional Information

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.

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