Raunaq Naidu

San Jose, CA

Senior Staff Software Engineer · Ambient AI, Agentic Systems & On-Device/Cloud AI Architecture for Consumer Hardware

Current Focus

Ambient AI, multi-modal retrieval, LLM summarization, and natural language queries over real-world audio memory.

Systems Scope

Consumer hardware, wearable devices, embedded RTOS systems, display software stacks, and performance tooling.

Leadership

Technical lead and engineering manager experience across global teams, cross-org workstreams, and shipped products.

Experience

Meta Platforms · Senior Staff Software Engineer

Nov 2018 – Present

Technical Lead, Voice AI

2025–Present
  • Designed end-to-end AI architecture for an ambient audio recall system on wearable devices: on-device audio capture → cloud transcription → LLM summarization (Llama 3) → FAISS vector retrieval — enabling natural language queries over real-world audio memory.
  • Built multi-modal retrieval pipeline querying audio, visual, and conversational memory in parallel with cross-domain intent routing; established offline eval pipelines with systematic error analysis, driving measurable recall quality improvements across releases.
  • Made key on-device vs. cloud tradeoff decisions: chose on-device speaker diarization (PyAnnote-based) over cloud API, reducing power consumption by ~30%; coordinated 10+ dependent workstreams through multiple firmware freezes to ship on schedule.

Technical Lead, Autonomous Crash Investigation Agent

2025
  • Built a production multi-agent system that autonomously diagnoses device crashes, performs root cause analysis across logs/coredumps/source code, and generates code fixes — deployed across 4+ wearable product lines and auto-triaging all new crash tasks.
  • Architected modular agent design with specialized sub-agents per crash domain (JVM, native, embedded RTOS), each equipped with domain-specific tools: log analysis, crash dump symbolication, GDB-based coredump inspection, and semantic code search via embeddings.
  • Built an evaluation framework using historical crash-fix pairs: automated reproduce-and-compare grading via semantic similarity (sentence-transformers + cosine scoring) enables continuous quality iteration. Reduced median crash resolution time from 13 → 3 days.

Technical Lead & Engineering Manager, Systems Performance

2023–2024
  • Built and managed a global team of 10 engineers driving performance optimization for AI-powered wrist wearables; led cross-org effort to define and ship performance targets for a consumer AI device.
  • Built real-time profiling and monitoring tooling for embedded RTOS systems to measure AI workload performance, enabling data-driven tradeoffs between model size, latency, and power on-device.

Technical Lead & Engineering Manager, Display & Graphics

2019–2023
  • Built and managed a team of 8 engineers; shipped the display software stack on Quest 2, Quest Pro, Quest 3, and Ray-Ban Meta smart glasses — products collectively reaching tens of millions of users.
  • Invented and patented a dynamic display brightness and refresh rate modulation system (US20230360566A1) — an adaptive control technique that generalizes directly to resource-aware on-device AI inference.

Meta Company · Senior Software Engineer

Nov 2016 – Nov 2018
  • Implemented asynchronous re-projection algorithms for a real-time graphics compositor, reducing motion-to-photon latency — the same low-latency rendering principles that underpin responsive AI inference pipelines.
  • Reduced depth camera pipeline latency by 80% through driver stack analysis and optimization; built the core runtime SDK integrating sensors, algorithms, and rendering into a unified C API for partner platforms.

Nvidia · Senior Software Engineer

Jun 2014 – Nov 2016
  • Invented Direct Mode rendering for VR, significantly reducing display pipeline latency for HMDs — featured in multiple industry publications and adopted broadly across the VR ecosystem.
  • Implemented HDR display drivers (DisplayPort 1.4) for consumer GPUs used by millions; contributed to driver stack foundations that underpin Nvidia's current AI/inference GPU product line.