Studio F.

Key Skills

Software

Mercor

Top Subject Matter

AI model output evaluation for text and multimodal reasoning tasks

Multimodal audio/content annotation evaluation under guideline-driven rubrics

Audio

Top Data Types

Text

Audio

Image

Top Task Types

RLHF

Freelancer Overview

AI Contributor / RLHF, SFT & Generalist Evaluation — Mercor (Project Neon and Project Nova). Brings 17+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Mercor. Education includes N/A, Humber College. AI-training focus includes data types such as Text and Audio and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

AI Evaluation Contributor - Mercor

AudioRLHF

Completed AI evaluation work across Project Neon and Project Nova, including RLHF annotation, SFT review, generalist model evaluation, multimodal analysis, task flagging, and rubric-based response assessment. Evaluated AI-generated outputs for helpfulness, instruction following, factual consistency, reasoning quality, safety, alignment, clarity, completeness, and user-intent fulfillment. Also evaluated audio and visual annotation tasks by applying detailed guidelines to assess multimodal content quality, artifacts, alignment, and task-specific labeling requirements. • Performed comparative RLHF judgments across candidate responses and identified hallucinations, logic gaps, and rubric deviations • Reviewed SFT-oriented outputs for ideal-response quality, structure, tone, and requirement alignment • Applied safety-first, policy-aware judgment when flagging problematic or non-compliant content • Ensured consistent long-session performance through precise guideline interpretation

2025 - 2026

AI Contributor / RLHF, SFT & Generalist Evaluation — Mercor (Project Nova audio & multimodal evaluation)

MercorAudio

You evaluated audio-related components and multimodal annotation tasks as part of Project Nova. You applied detailed annotation guidelines to assess multimodal content quality, artifact presence, output alignment, descriptive accuracy, and task-specific labeling requirements. You identified quality issues and alignment problems that affected the usefulness and compliance of the generated outputs. • Reviewed audio and associated multimodal content for artifacts and quality degradation. • Verified descriptive accuracy and alignment to project- or task-specific requirements. • Flagged issues related to non-compliance, ambiguity, or low-quality outputs. • Performed guideline-based consistency checks across repeated annotation and evaluation items.

2025 - 2026

AI Contributor / RLHF, SFT & Generalist Evaluation — Mercor (Project Neon and Project Nova)

MercorTextRLHF

You performed RLHF-style evaluation and rubric-based response assessment for multiple projects, including comparative judgments across candidate model outputs. You assessed outputs for helpfulness, instruction following, factual consistency, reasoning quality, safety, alignment, clarity, completeness, and user-intent fulfillment. You used safety-first policy-aware criteria to flag non-compliant, ambiguous, or low-quality content. • Compared and ranked candidate responses to identify hallucinations, logic gaps, formatting failures, and rubric deviations. • Reviewed SFT-oriented outputs for ideal-response quality, correctness, structure, tone, and requirement alignment. • Interpreted guidelines to maintain consistency across complex, long-session evaluation tasks. • Documented task flags and identified multimodal and reasoning failures affecting overall output quality.

2025 - 2026

Education

Work History