Landy O.

Key Skills

Software

Label Studio

Remotasks

Scale AI

CVAT

Top Subject Matter

Computer Vision

Finance/Document Analysis

Chatbots/Natural Language Processing

Top Data Types

Document

Image

Text

Top Task Types

Bounding Box

Classification

Evaluation/Rating

RLHF

Text Generation

Entity (NER) Classification

Freelancer Overview

AI Annotation Specialist | Dataset Quality | Model Support. Brings 18+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include CVAT, Label Studio, and Scale AI. Education includes Non-Degree College Coursework, Florida International University (2002). AI-training focus includes data types such as Image, Document, and Text and labeling workflows including Bounding Box, Entity (NER) Classification, and Evaluation.

Labeling Experience

AI Annotation Specialist | Dataset Quality | Model Support

CVATImageBounding Box

As an AI Annotation Specialist at FloVisions, I created and reviewed high-quality annotations for computer vision datasets, supporting ML development and model training. My work involved preparing training data, refining labeling guidelines, and escalating ambiguous cases for resolution. I contributed to calibration reviews, curation, documentation, and quality-control workflows to enhance annotation reliability across varied camera and site conditions. • Performed object detection, segmentation, region labeling, and box labeling of images. • Used CVAT and Roboflow for annotation tasks, quality checks, and calibration reviews. • Worked closely with ML engineers and software teams on process and dataset requirements. • Produced comprehensive guides and documented annotation processes for continuous improvement.

2023 - Present

Data Analyst (AI Labeler & RLHF Team)

ImageClassification

As a Data Analyst at DoorDash, I led analysis and labeling initiatives for photo data taxonomy, participating in RLHF and LLM training for inventory data. My core labeling tasks involved data annotation, writing descriptions, tagging, and supporting data triangulation processes. I streamlined data workflows and improved data integration across visual and textual inventory datasets. • Labeled and classified photo data using taxonomy standards for retail and CPG brands. • Engaged with reinforcement learning and LLM training teams for human feedback processes. • Performed tagging and annotation of visual/text data to enhance data workflow efficiency. • Optimized extraction and integration of labeled data from multiple sources.

2022 - 2026

Financial Document Annotation & Table Structure Labeling

Label StudioTextBounding BoxEntity (NER) Classification

Annotated and labeled a large corpus of financial documents, including profit and loss statements, balance sheets, and cash flow reports. Focused on identifying and labeling key financial entities (revenues, expenses, net income) while preserving complex table hierarchies and relationships. Utilized Label Studio to implement efficient workflows for classifying cells, rows, columns, and subtotals, ensuring consistent data extraction. Adhered to strict quality assurance protocols, cross-validating annotations to maintain over 98% accuracy. Collaborated in iterative feedback loops to improve AI model performance, with an emphasis on logical consistency and edge case handling.

2024 - 2024

LLM Output Evaluation & Instruction-Based Text Annotation

Scale AITextText Summarization

Performed large-scale evaluation and annotation of AI-generated text outputs for fine-tuning LLMs. Tasks included generating high-quality instructional prompts, classifying model responses, and providing feedback based on logical accuracy, relevance, and adherence to human intent. Special focus on identifying logical inconsistencies, edge cases, and bias within model outputs. Contributed to iterative human-in-the-loop cycles, refining model behavior through structured feedback. Ensured annotation quality via cross-validation, adherence to strict guidelines, and detailed documentation of evaluation rationale.

2023 - 2024

Education

Work History