Claire M.

Key Skills

Software

Other

Data Annotation Tech

Labelbox

Mercor

Top Subject Matter

LLM performance improvement (RLHF) and prompt engineering

Dataset preparation and pipeline engineering for AI training readiness

Data quality evaluation and governance for reliable AI/data use

Top Data Types

Text

Document

Computer Code Programming

Top Task Types

RLHF

Prompt + Response Writing (SFT)

Computer Programming/Coding

Fine-tuning

Text Summarization

Classification

Freelancer Overview

AI Prompt & Research Engineer (Independent contractor, remote). Brings 12+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Bachelor of Science, Royal Agricultural University (2022) and Code First Girls Data Degree, Code First Girls (2022). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including RLHF, Prompt + Response Writing (SFT), and Evaluation.

Labeling Experience

AI Prompt & Research Engineer (Independent contractor, remote)

Don't discloseTextRLHF

Worked with global AI labs and Tier-1 tech companies to improve LLM performance using Reinforcement Learning from Human Feedback (RLHF) and advanced prompt engineering. Leveraged dual ESL and data engineering expertise to strengthen model reasoning, factual accuracy, and cross-cultural linguistic nuance. Focused on aligning model outputs to human preferences through iterative prompt and feedback-driven optimization. • RLHF collaboration with external AI labs • Advanced prompt engineering to enhance LLM behavior • Evaluation of reasoning, accuracy, and cross-cultural nuance • Iterative refinement based on partner feedback

2026 - Present

Data Engineer, Redkite - Accenture (London)

OtherComputer Code ProgrammingComputer Programming/Coding

Designed and delivered cloud data infrastructure and pipelines that supported downstream model development workflows. Authored modular PySpark code to transform and prepare data for scalable analytics use-cases. Although primarily data engineering, these pipelines and transformations enabled model training and evaluation tasks that require structured datasets. • ETL/ELT pipeline implementation for batch and streaming workloads • Authored PySpark code on Azure/Databricks/Fabric for performance improvements • Built reusable REST API ingestion frameworks for structured dataset creation • Translated business requirements into scalable data models

2023 - 2025

Education

Work History