Hardware Analytics Engineer: AI Telemetry & Reliability

Cerebras; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Cerebras Systems is seeking a Hardware Analytics Engineer to design and optimize scalable data pipelines for multi-terabyte hardware telemetry and reliability analytics. You will architect ETL processes to aggregate performance streams and build anomaly detection systems using Python, SQL, Tableau, Hive, and Spark.

You will lead hardware characterization experiments and thermal studies to drive improvements in reliability and efficiency across CPU, GPU, DRAM, PCIe, and storage subsystems.

Qualifications

• Master’s degree or foreign equivalent in Electrical Engineering, Computer Engineering, or closely related field.

Responsibilities

• Design scalable data pipelines for multi-terabyte telemetry, reliability analytics, and performance optimization.
• Architect and optimize ETL processes to aggregate and analyze hardware performance and telemetry streams across platforms.
• Develop hardware performance analysis and anomaly detection using Python, SQL, Tableau, Hive and Spark to forecast failures and optimize systems.
• Lead hardware characterization experiments and thermal studies to improve reliability and efficiency, reducing carbon footprint and water usage.
• Engineer telemetry ingestion, monitoring, and visualization to provide real-time health data to operations teams for data-driven decisions.

Skills

Python

SQL

Tableau

Hive

Spark

ETL

Data pipelines

Telemetry

Anomaly detection

Education

Master’s degree in Electrical Engineering

Job description

Cerebras Systems is seeking a Hardware Analytics Engineer to design and optimize scalable data pipelines for multi-terabyte hardware telemetry and reliability analytics. You will architect ETL processes to aggregate performance streams and build anomaly detection systems using Python, SQL, Tableau, Hive, and Spark.

You will lead hardware characterization experiments and thermal studies to drive improvements in reliability and efficiency across CPU, GPU, DRAM, PCIe, and storage subsystems.