System Product Engineer (New or Recent Graduate)

Requirements

• 0-5+ years in validation, modeling, infrastructure development, or related fields
• Fresh PhD graduates with relevant research experience are encouraged to apply
• Technical Skills
• Strong Python skills; C/C++ a plus
• Experience or research background in system modeling, simulation, or workload analysis
• Understanding of data movement and performance behavior across system components
• Ability to rapidly absorb complex system architecture and translate understanding into engineering artifacts
• Key Traits
• Builder mindset - tools, frameworks, infrastructure
• Strong system and data intuition
• Hands-on coder or effective AI workflow orchestrator - non-negotiable
• Comfortable working in ambiguous, early-stage environments
• Sandisk is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at jobs.accommodations@sandisk.com to advise us of your acc

Additional Information

Level: Senior / Staff Focus: AI workload modeling, simulation-first validation, and scalable test infrastructure Why 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 & Generation Develop and analyze AI workloads, focusing on memory access characterization, and data movement behavior Generate trace-based and synthetic stress patterns for system-level validation Simulation-First Validation Build lightweight simulation and emulation environments (e.g., QEMU-based system models and customized modeling) for early validation and scalable development environment Map workload to test prior to hardware availability Reduce dependency on full-system emulation/real hardware through independent, scalable frameworks AI-Assisted Test Infrastructure Build C/C++/Python-based automation frameworks with parallel execution, structured logging, and scalable data pipelines Build and maintain spec-to-code pipelines: convert product specifications into structured formats for AI-assisted code generation and automated validation Integrate AI tools for test content generation, debug acceleration, and log analysis Firmware & Test Content Development Develop workload-aware test firmware aligned with system-level use cases Enable functional coverage based on real workloads, not synthetic-only scenarios Data & AI-Driven Insights Build pipelines for data collection, failure classification, and pattern detection Apply ML techniques where appropriate for anomaly detection and failure clustering System Correlation Map workload behavior to system stress and device-level impact, enabling translation between real workloads and production test coverage Correlate across compute, memory, and storage subsystems Education BS/MS/PhD in Electrical Engineering, Computer Engineering, or Computer Science

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