Daniel Park

Software Engineer - ML Infra - CMU MS ECE

Graduate student at Carnegie Mellon with backend, distributed systems, and applied ML experience. Comfortable shipping production APIs, building data pipelines, and iterating on ML models end-to-end.

Experience

Software Engineer Intern

Amazon · 2024–2024

Built a low-latency recommendation service in Java (Spring Boot), reducing P95 latency by 28% via async processing + Redis caching. Designed and deployed data pipeline (AWS Lambda + S3 + DynamoDB) processing 2M+ daily events. Led A/B testing integration with experimentation platform, improving CTR by +9%. Improved system reliability by implementing retry mechanisms and circuit breaker patterns.

Machine Learning Engineer Intern

AI SaaS Startup · 2024–2024

Developed NLP pipeline for resume parsing using Transformers, improving extraction accuracy 72% -> 88%. Built production APIs (FastAPI) supporting 10K+ daily requests. Implemented semantic search system (FAISS + embeddings) for candidate-job matching. Reduced inference latency by 40% via batching and caching.

Undergraduate Research Assistant

UCLA Computer Science Department · 2022–2023

Improved object detection model (PyTorch, YOLO) with +11% precision gain. Built automated data annotation pipeline, reducing labeling effort by ~50%. Contributed to research on efficient inference methods.

Daniel Park

Experience

Software Engineer Intern

Machine Learning Engineer Intern

Undergraduate Research Assistant

Education

Carnegie Mellon University

University of California, Los Angeles