About The Position

This role focuses on AI workload modeling, simulation-first validation, and scalable test infrastructure. The position offers the opportunity to build foundational infrastructure, including simulation, automation, and AI-assisted workflows, within a small, high-impact team. The goal is to shape how system test development is done by building AI-native validation and test infrastructure that enables early system learning and workload-driven validation across the product lifecycle, reducing dependency on late-stage integration environments. The engineer will operate across compute, memory, and storage subsystems, enabling correlation between real workloads and system behavior.

Requirements

  • BS/MS/PhD in Electrical Engineering, Computer Engineering, or Computer Science
  • 0–5+ years in validation, modeling, infrastructure development, or related fields
  • Fresh PhD graduates with relevant research experience are encouraged to apply
  • 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
  • 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

Responsibilities

  • 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.
  • 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.
  • 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.
  • Develop workload-aware test firmware aligned with system-level use cases.
  • Enable functional coverage based on real workloads, not synthetic-only scenarios.
  • Build pipelines for data collection, failure classification, and pattern detection.
  • Apply ML techniques where appropriate for anomaly detection and failure clustering.
  • 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.

Benefits

  • paid vacation time
  • paid sick leave
  • medical/dental/vision insurance
  • life, accident and disability insurance
  • tax-advantaged flexible spending and health savings accounts
  • employee assistance program
  • other voluntary benefit programs such as supplemental life and AD&D, legal plan, pet insurance, critical illness, accident and hospital indemnity
  • tuition reimbursement
  • transit
  • the Applause Program
  • employee stock purchase plan
  • Sandisk's Savings 401(k) Plan
  • Short-Term Incentive (STI) Plan
  • Long-Term Incentive (LTI) program (restricted stock units (RSUs) or cash equivalents, for eligible roles)
  • RSU awards for eligible new hires
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