Efer T.

Key Skills

Software

Remotasks

Other

Top Subject Matter

AI and NLP evaluation

data curation

and LLM performance assessment

Top Data Types

Text

Document

Top Task Types

Polygon

Data Collection

Computer Programming/Coding

Function Calling

Prompt + Response Writing (SFT)

Transcription

Evaluation/Rating

Red Teaming

RLHF

Text Summarization

Text Generation

Polyline

Entity (NER) Classification

Point/Key Point

Segmentation

Classification

Cuboid

Bounding Box

Fine-tuning

Question Answering

Object Detection

Freelancer Overview

AI Evaluation and Data Curation Specialist – Remotask & Outlier (2018–2022). Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Remotasks and Other. Education includes Master’s in Data Science, Boston University and Bachelor’s in Mathematics and Computer Science, Stamford Bridge Institute. AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Data Science Subject Matter Expert – GenAI Research Projects (2020–Present)

OtherText

Designed and implemented scalable data collection and evaluation workflows for Generative AI research to support LLM training and evaluation. Created original, high-quality datasets and domain problems, then conducted rigorous fact-checking and bias analysis to ensure data integrity. Collaborated with GenAI researchers and engineers to translate research objectives into measurable requirements and repeatable data operations. • Defined data requirements and success metrics for LLM training. • Built scalable data collection and evaluation workflows aligned to research goals. • Performed fact-checking and bias analysis to maintain accuracy and fairness. • Improved throughput, quality, and reproducibility through workflow optimization.

2020 - Present

AI Evaluation and Data Curation Specialist – Remotask & Outlier (2018–2022)

RemotasksText

Executed data annotation, validation, and evaluation tasks for AI and NLP systems using structured quality checks. Developed evaluation frameworks to assess LLM performance and factual consistency for downstream training and testing. Applied prompt engineering to refine AI-generated outputs and improve reliability while enforcing ethical handling and fairness standards. • Annotated and validated AI/NLP outputs for quality control. • Built structured rubric-based evaluation frameworks for LLM factuality. • Used prompt engineering to improve reliability of generated responses. • Ensured compliance with AI fairness, transparency, and ethical data handling requirements.

2018 - 2022

Education

Work History