• Delivered a 3-hour battery life improvement over the modeled day of use on a Meta wearable product through
systematic power analysis and optimization across the full AOSP/Linux kernel/RTOS stack.
• Designed and implemented proxy metrics to detect power and thermal regressions during playtesting and field
deployment, keeping power/thermal issues below 1%.
• Deployed AI-powered code review tooling to audit firmware changes for power efficiency across both application and driver layers, enabling proactive identification of inefficient patterns before submission.
• Built and integrated MCP servers connecting AI assistants directly to Devices Under Test (DUT), enabling real-time querying of signal states and power rail data for deeper, context-aware firmware analysis.
• Designed and led company-wide power optimization training sessions for engineers across kernel, OS, RTOS, and application layers; produced archived video content to scale knowledge sharing across teams.
• Reviewed and provided sign-off on schematic changes for power and thermal design, ensuring hardware decisions align with product battery life and thermal budget requirements.
• Identified and delivered targeted power optimizations that enabled new feature launches on wearable products
while maintaining or improving day-of-use battery life.