Paul Nguyen

About Paul Nguyen

I build applied AI systems where reliability matters. At Amazon I lead the integration of generative AI into the Alexa Auto experience - fine-tuning and evaluating LLMs, building the infrastructure that gets them shipped, and coordinating across domain teams (music, car control, navigation, search) to turn generic model capability into automotive-specific experiences.I also pursue personal projects where I develop models and explore machine learning solutions for various challenges I encounter. Currently building Autodidact, an open-source local-first AI agent that learns from itscloud queries - the local brain answers what it knows, escalates to the cloud when it doesn\'t, and distills what it learns back down.My PhD was in detecting hidden adversarial behaviors in complex systems - published in TVLSI and TDSC, and my Master’s degree was in focused on video compression and computer vision algorithm optimization for edge devices.I’m skilled in C/C++, Java, Python, Scala, Tcl, and TypeScript, with strong experience in applied machine learning and computer vision, software design and development, computer architecture, and distributed systems.

Experience

1. Machine Learning Engineer

   Amazon

   Aug 2022 — Present · CA, US

   Led the development of speech‑to‑speech Edge AI model fine‑tuning, evaluation, and profiling infrastructure for on‑device automotive voice assistants.Applied ML engineer shipping generative AI into Alexa Auto. Work spans the full stack - fine-tuning and evaluation, cloud infrastructure, device-side integration, and cross-team coordination across the domain teams whose features ride on the GenAI stack.• Led cross-functional integration of generative AI into the Alexa Auto experience, contributing across the full ML stack from model fine-tuning to scalable cloud infrastructure.• Fine-tuned and evaluated LLMs to improve in-car voice assistant capabilities and contextual understanding; owned evaluation frameworks for edge-environment deployment.• Built tooling and infrastructure for deploying GenAI features across Alexa Auto clients, coordinating with music, car control, navigation, and search domain teams to turn generic LLM capability into automotive-specific experts.• Designed and delivered voice- and text-based search and navigation experiences for automotive environments, including ranking models and autocomplete algorithms for Alexa Auto text search.• Built scalable indexing pipelines using Apache Spark, Amazon EMR, OpenSearch, and S3 to power efficient ingestion, transformation, and retrieval for product features.• Architected experimentation frameworks on AWS Bedrock (including Anthropic\'s Claude models) to explore LLM-powered improvements in automotive search and navigation.

Education

• Georgia Institute of Technology

  Doctor of Philosophy - PhD, Electrical and Computer Engineering

• Seoul National University

  Computer Engineering

Skills

• Microsoft Office
• Vlsi
• Verilog
• Linux
• Matlab
• Field-Programmable Gate Arrays (Fpga)
• Algorithms
• Research
• Python
• Java
• C
• Programming
• Perf
• Teamwork
• Tcl-Tk
• Very-Large-Scale Integration (Vlsi)
• C++
• Data Analysis