Machine Learning Intern
Lockheed Martin
Built distributed data pipelines, trained geometry models, and deployed containerized inference services for CAD comparison and aerospace inspection.
- Built a distributed ETL pipeline designed to ingest CAD models, revision metadata, and inspection outcomes. Applied schema validation, deduplication, and geometric normalization to produce training datasets.
- Trained a Siamese geometry encoder across multiple GPUs using PyTorch DDP, mixed precision, distributed sampling, and contrastive loss. Fused learned embeddings with FPFH feature matching, RANSAC–ICP registration, and Chamfer/Hausdorff distance for CAD comparison.
- Containerized inference services with Docker and Kubeflow, delivering an 86% improvement, 58% faster inspections, and $15,000 in weekly savings. Validated models to under 5% error across 100+ tests with 10+ engineers across QA and operations.
