Lucy M.

Key Skills

Software

Other

Appen

Clickworker

CloudFactory

Data Annotation Tech

HiveMind

Top Subject Matter

AI model evaluation and training data annotation for large language models

Natural language processing (NLP) data labeling and AI model evaluation

Legal Services & Contract Review

Top Data Types

Text

Document

Image

Top Task Types

Bounding Box

Question Answering

Transcription

Data Collection

Computer Programming/Coding

Prompt + Response Writing (SFT)

Freelancer Overview

Senior Software Engineer / AI Evaluation Specialist — Independent & Contract Roles. Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Doctor of Philosophy, University of Maryland (2027) and Master of Science, University of Maryland (2023). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Senior Software Engineer / AI Evaluation Specialist — Independent & Contract Roles

Text

Performed large-scale evaluation and response ranking of AI-generated outputs across diverse domains to improve accuracy and coherence. Built structured annotation frameworks and conducted instruction-following checks to ensure alignment with task requirements and user intent. Contributed to RLHF-style feedback loops and prompt refinement using detailed error analysis to improve model reasoning performance. • Ranked AI responses for correctness, coherence, and reasoning quality • Designed training/annotation guidelines and quality control processes • Ran detailed error analysis to identify failure patterns and iterate • Refined prompts and applied RLHF-style feedback mechanisms

2023 - Present

Multidisciplinary Engineering & AI Systems Specialist — Independent Projects & Contract Work

OtherText

Annotated and reviewed 10K+ samples while following structured labeling guidelines to improve dataset consistency. Ranked multiple model outputs based on correctness, reasoning depth, and clarity for training and benchmarking workflows. Built and applied NLP text-evaluation pipelines to support model evaluation and classification benchmarking. • Labeled and quality-checked large text datasets (10K+ samples) • Conducted response ranking to assess quality and reasoning • Improved dataset consistency by applying structured labeling guidance • Applied text evaluation techniques for benchmarking and model assessment

2021 - 2022

Education

Work History