System Product Engineer (New or Recent Graduate)

System Product Engineer (New or Recent Graduate)

12 Jun 2026
New Mexico, Milpitas 00000 Milpitas USA

System Product Engineer (New or Recent Graduate)

Level: Senior / StaffFocus: AI workload modeling, simulation-first validation, and scalable test infrastructureWhy This Role: Build foundational infrastructure — simulation, automation, and AI-assisted workflows. Small team, high impact, and the opportunity to shape how system test development is done.Build AI-native validation and test infrastructure enabling early system learning and workload-driven validation across the product lifecycle, reducing dependency on late-stage integration environments. You will operate across compute, memory, and storage subsystems, enabling correlation between real workloads and system behavior.ESSENTIAL DUTIES AND RESPONSIBILITIES:AI Workload Modeling & GenerationDevelop and analyze AI workloads, focusing on memory access characterization, and data movement behaviorGenerate trace-based and synthetic stress patterns for system-level validationSimulation-First ValidationBuild lightweight simulation and emulation environments (e.g., QEMU-based system models and customized modeling) for early validation and scalable development environmentMap workload to test prior to hardware availabilityReduce dependency on full-system emulation/real hardware through independent, scalable frameworksAI-Assisted Test InfrastructureBuild C/C/Python-based automation frameworks with parallel execution, structured logging, and scalable data pipelinesBuild and maintain spec-to-code pipelines: convert product specifications into structured formats for AI-assisted code generation and automated validationIntegrate AI tools for test content generation, debug acceleration, and log analysisFirmware & Test Content DevelopmentDevelop workload-aware test firmware aligned with system-level use casesEnable functional coverage based on real workloads, not synthetic-only scenariosData & AI-Driven InsightsBuild pipelines for data collection, failure classification, and pattern detectionApply ML techniques where appropriate for anomaly detection and failure clusteringSystem CorrelationMap workload behavior to system stress and device-level impact, enabling translation between real workloads and production test coverageCorrelate across compute, memory, and storage subsystems

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