Murphy C.

Key Skills

Software

Other

Google Cloud Vertex AI

Top Subject Matter

Natural Language Processing

LLM Training

Generative AI

Top Data Types

Text

Image

Top Task Types

Segmentation

Freelancer Overview

ML Data Annotation Manager (Tesla). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Master of Science, The University of Texas at Austin (2020) and Bachelor of Science, Texas A&M University (2017). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Segmentation.

Labeling Experience

Generative AI Content Quality Evaluator (Project)

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Developed an automated human-in-the-loop evaluation pipeline for assessing LLM-generated content quality. Designed custom metrics for coherence, factuality, and relevance, processing more than 10K outputs daily. Integrated iterative feedback collection to reduce content defect rates and improve model performance.• Designed and executed content evaluation frameworks for LLM outputs • Led human-in-the-loop assessment and quality rating for GenAI data • Established improvement metrics and analyzed review outcomes • Enhanced LLM dataset quality via iterative evaluation cycles

2023 - Present

ML Data Annotation Manager (Tesla)

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Managed large-scale data annotation workflows for machine learning training datasets, overseeing pipelines that processed over 500K labeled samples monthly. Developed and implemented robust quality assurance systems to maintain high inter-annotator agreement and data integrity. Collaborated with cross-functional teams to ensure data quality for production-grade AI models.• Built and fine-tuned LLMs using RLHF and instruction tuning • Defined annotation guidelines and evaluated labeler outputs • Designed scalable annotation frameworks for NLP tasks • Established feedback mechanisms for continuous dataset improvement

2021 - 2023

Computer Vision Defect Annotation Lead (Project)

Google Cloud Vertex AIImageSegmentation

Designed and improved a CNN-based defect detection system for manufacturing quality control, focusing on reducing annotation costs and improving detection precision. Implemented active learning strategies to refine image segmentation and labeling tasks. System facilitated quality-controlled image labeling for iterative model improvement.• Developed image annotation protocols for manufacturing defects • Applied active learning for cost-efficient annotation cycles • Maintained high annotation precision for quality control • Coordinated iterative dataset improvement for CV models

2021 - 2022

AI Research Intern (UT Austin)

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Assisted in developing benchmark datasets and evaluation metrics for generative AI model assessment, ensuring quality and reproducibility. Helped senior researchers formalize dataset curation protocols and contributed to annotation standards. Supported implementation and documentation of evaluation frameworks for internal testing.• Participated in dataset design for multi-modal LLM evaluation • Established quality standards for model benchmarking in research • Documented and tracked evaluation results for analysis • Helped prepare technical documentation for dataset protocols

2020 - 2020

Education

Work History