Nicholas L.

Key Skills

Software

Appen

Top Subject Matter

Multilingual language model improvement (dialogue quality, linguistic relevance).

Conversational AI evaluation (speech, sentiment, instruction following, narrative branching).

NLP dialogue/narrative evaluation and quality auditing (HITL workflows).

Top Data Types

Text

Audio

Image

Document

Top Task Types

Fine-tuning

RLHF

Freelancer Overview

Al Data Annotator | Language Model Trainer, APPEN. Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Appen, N, and A. Education includes Bachelor of Science, Open University (2021) and Professional Certificate, Coursera (2023). AI-training focus includes data types such as Text and Audio and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

AI Content Evaluator & Data Specialist, Handshake AI

TextRLHF

Reviewed and compared text-to-image and video AI outputs across 500+ prompts to select best results and capture reasons for RLHF preference data creation. Assessed 2D and 3D images for visual quality, spatial correctness, and alignment with the prompt requirements. Analyzed financial and compliance-related prompt details (e.g., cash flow projections, risk calculations, GAAP compliance) and flagged about 12% of outputs for model alignment fixes. • Best-result selection across multi-modal outputs. • RLHF preference labeling with rationale notes. • Visual/spatial quality and prompt alignment checks. • Financial/GAAP compliance-oriented output verification.

2025 - 2026

AI Data Evaluation Specialist – MERCOR AI

TextRLHF

Ran structured prompt tests on AI-generated content including slides, text, and visuals, completing 300+ tasks per day. Reviewed outputs for accuracy, clarity, and formatting and achieved 97%+ rubric compliance, then compared multiple responses per prompt to select best results. Added preference labels to create RLHF training sets and stored 1,000+ evaluation records for stakeholder review. • Prompt test execution and high-throughput evaluations. • Rubric compliance scoring (97%+). • Preference labeling across multiple candidate responses. • Evaluation record management for team review.

2025 - 2025

AI Data Annotator | AI Evaluation, TELUS International

Audio

Evaluated AI-generated content for accuracy, instruction following, and consistency using rubric-based QA workflows. Completed over 500 items per week with reported 96% accuracy while meeting strict SLAs on high-volume annotation batches. Conducted rubric-based reviews and QA audits and provided workflow feedback through testing custom annotation tools. • Rubric-based QA auditing and accuracy evaluation. • High-volume annotation processing (500+ items/week). • SLA-driven quality compliance in batch labeling. • Tool testing and workflow feedback to improve completion time.

2024 - 2025

Al Data Annotator, Invisible Technologies

Text

Performed rubric-based evaluation and annotation of speech recognition, sentiment analysis, and generative AI conversational content for reliability and consistency. Audited dataset quality using defined processes to meet heavy project deadlines while adhering to labeling guidelines. Labeled conversational flow, branching responses, and instruction-following behavior in interactive narrative scenarios. • Speech recognition and sentiment-related annotations. • QA reviews to improve dataset reliability. • Rubric-based evaluation for conversational flow. • Instruction-following and branching behavior labeling in dialogue.

2024 - 2025

Al Data Training Specialist, SCALE AI

TextFine-tuning

Labeled and evaluated large-scale NLP and computer vision datasets to support model training and validation. Conducted structured research and evaluation of generative text, image, and multimodal models to assess accuracy, instruction following, and performance consistency. Performed secondary QA reviews to identify edge cases and maintained high annotation accuracy through strict guideline adherence. • NLP and multimodal dataset labeling for training/validation. • Model evaluation on instruction-following and accuracy. • Secondary QA to identify edge cases/inconsistencies. • Evaluator alignment via documentation and grading calibration.

2023 - 2024

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Education

Work History