Hardware Analytics Engineer

Cerebras Systems Inc.

HybridPosted July 28, 2026

About the role

Hybrid Hardware Analytics Engineer – Sunnyvale, California, United States

Cerebras Systems Inc. is seeking a Hardware Analytics Engineer to join our Sunnyvale, CA team in a hybrid work environment. Cerebras Systems builds the world's largest AI chip and delivers industry-leading training and inference speeds. This role focuses on designing and optimizing scalable data pipelines for multi-terabyte hardware telemetry, reliability analytics, and performance optimization across heterogeneous compute, storage, and AI server platforms.

Hardware Analytics Engineer

Cerebras Systems Inc.

HybridPosted July 28, 2026

About the role

Hybrid Hardware Analytics Engineer – Sunnyvale, California, United States

Cerebras Systems Inc. is seeking a Hardware Analytics Engineer to join our Sunnyvale, CA team in a hybrid work environment. Cerebras Systems builds the world's largest AI chip and delivers industry-leading training and inference speeds. This role focuses on designing and optimizing scalable data pipelines for multi-terabyte hardware telemetry, reliability analytics, and performance optimization across heterogeneous compute, storage, and AI server platforms.

Responsibilities

Design, develop, and optimize hyperscale data pipeline architectures and ETL processes to aggregate, process, and analyze multi-terabyte hardware performance and telemetry streams (utilization, power, thermal, acoustic, and reliability metrics).
Build hardware performance analysis and anomaly detection systems using Python, SQL, Tableau, Hive, and Spark to forecast failure curves, identify bottlenecks, and generate prescriptive recommendations.
Lead hardware characterization experiments and thermal/cooling studies to evaluate operational envelopes, delivering strategies that reduce carbon footprint, improve water usage efficiency, and maintain or enhance reliability.
Engineer telemetry ingestion, monitoring, and visualization systems to provide real-time, high-fidelity hardware health data to hardware, firmware, and data-center operations teams for data-driven decision-making at scale.
Define, operationalize, and maintain custom efficiency and reliability metrics; perform root cause analysis of systemic failures using

Responsibilities

Design, develop, and optimize hyperscale data pipeline architectures and ETL processes to aggregate, process, and analyze multi-terabyte hardware performance and telemetry streams (utilization, power, thermal, acoustic, and reliability metrics).
Build hardware performance analysis and anomaly detection systems using Python, SQL, Tableau, Hive, and Spark to forecast failure curves, identify bottlenecks, and generate prescriptive recommendations.
Lead hardware characterization experiments and thermal/cooling studies to evaluate operational envelopes, delivering strategies that reduce carbon footprint, improve water usage efficiency, and maintain or enhance reliability.
Engineer telemetry ingestion, monitoring, and visualization systems to provide real-time, high-fidelity hardware health data to hardware, firmware, and data-center operations teams for data-driven decision-making at scale.
Define, operationalize, and maintain custom efficiency and reliability metrics; perform root cause analysis of systemic failures using

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