Engineering, research, and teaching, most recent first.

Education

M.S. in Information Systems Management

Carnegie Mellon University

  • Relevant Coursework:
    • Introduction to Deep Learning (PhD-level)
    • Distributed Systems
    • Generative AI (PhD-level)
    • LLM Systems
    • AI Agents
  • Honors & Awards:
    • 3rd/238 Automatic Speech Recognition
    • 5th/266 Face Verification

B.B.A. in Finance and International Business

Fu Jen Catholic University

Skills

Programming
Python, C++, CUDA, Rust, Java, SQL
Frameworks
PyTorch, JAX, Flax, Optax, Hugging Face, PEFT/LoRA, DPO/GRPO, DeepSpeed, LangGraph
Tools
vLLM, SGLang, FastAPI, Docker, Kubernetes, Slurm, AWS, GCP, W&B, Git

Work Experience

Forward Deployed Engineer

Smile Line AI

  • Scoped the AI engine for Smile Line's flagship Smile Lite MDP app with the founding team, from model and data choices to EU and US privacy rules, ahead of deploying it to the company's device and app
  • Benchmarked detection models and quantized the chosen YOLO detector to run chairside on hardware the company already ships, so it checks whether each photo is framed correctly while the clinician shoots

Teaching Assistant, 11-785 Introduction to Deep Learning

Carnegie Mellon University, Language Technologies Institute

  • Ported course homeworks to JAX, Flax, and Optax for TPU training with Google, for 300+ students, and authored an LLM pretraining assignment to build a Llama-style model from scratch with RoPE, SwiGLU, and RMSNorm

Research Intern

Carnegie Mellon University

  • Designed a fail-closed policy engine that keeps a governed LLM agent from answering off-corpus and an evidence gate that withheld 296 of 300 claims under speaker overlap with full coverage on clean audio

Machine Learning Engineer

Future Educators

  • Built an OpenAI-backed RAG advising assistant over 1.2M+ GPT-labeled federal charter school records and deployed it on AWS as a FastAPI service at p95 under 2 s for 300+ daily sessions, 47% faster than manual lookup
  • Shipped an LLM document-processing pipeline on Claude 3.5 Sonnet that reached 93% extraction accuracy on a human-labeled eval set and cut manual review time by 70%

Machine Learning Engineer

The Heroes Garage

  • Reduced bid error against manual appraisal by 37% with a gradient-boosted model over Carfax history and past sale prices that set the buying team's live bids at Manheim and ACV auctions
  • Deployed a repair cost model trained on past repair orders that cut dealer estimate error (MAPE) from 22% to 7%

Publications

AcoustiClaim: A Numeric Claim Benchmark with Instrument Ground Truth

Sheng-Tse Lin, Siyuan Zhai*, Chien-Liang Kuo*, Massa Baali, Bhiksha Raj

Under reviewICASSP 2027PaperCode

* equal contribution

Open Source