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Clement Jeffrey

Machine Learning Intern – EmbedNexus

USA flag
Houston, Usa
$20.00/hrExpertAppenClickworkerCloudfactory

Key Skills

Software

AppenAppen
ClickworkerClickworker
CloudFactoryCloudFactory
CrowdFlowerCrowdFlower
Data Annotation TechData Annotation Tech
Kili TechnologyKili Technology
LabelboxLabelbox
MercorMercor
MindriftMindrift
OneFormaOneForma
OpenCV AI Kit (OAK)OpenCV AI Kit (OAK)
Redbrick AIRedbrick AI

Top Subject Matter

Large Language Model Evaluation
News Article Summarization
Medical and Financial Classification

Top Data Types

TextText
ImageImage
VideoVideo

Top Task Types

Text Summarization
Classification
Polygon
Object Detection
Red Teaming
Transcription
Evaluation Rating

Freelancer Overview

Machine Learning Intern – EmbedNexus. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Hugging Face and PyTorch. Education includes Bachelor of Science, Institute of Space Technology (2020). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Text Summarization.

ExpertEnglish

Labeling Experience

Machine Learning Intern – EmbedNexus

Text
I conducted benchmarking of large language models (LLMs) on CPU-only hardware, focusing on evaluating their outputs. This process involved running structured tasks and assessing results for latency, accuracy, and consistency. Quantized LLMs were deployed for chat, image analysis, and file-based Q&A workflows, and their performance was systematically compared. • Ran evaluation tasks on LLMs using structured datasets. • Measured and recorded model responses to benchmark output quality. • Assessed consistency and reliability of results under different quantization settings. • Used prompt-driven validation mechanisms and documented findings.

I conducted benchmarking of large language models (LLMs) on CPU-only hardware, focusing on evaluating their outputs. This process involved running structured tasks and assessing results for latency, accuracy, and consistency. Quantized LLMs were deployed for chat, image analysis, and file-based Q&A workflows, and their performance was systematically compared. • Ran evaluation tasks on LLMs using structured datasets. • Measured and recorded model responses to benchmark output quality. • Assessed consistency and reliability of results under different quantization settings. • Used prompt-driven validation mechanisms and documented findings.

2024 - 2025

Machine Learning Intern – Hex Softwares

TextText Summarization
I built a News Article Summarizer using Hugging Face Transformers, focusing on producing accurate and concise summaries. The process required data preprocessing, model fine-tuning, and validation of summary outputs. Quality assurance involved repeated review and adjustment to ensure summaries accurately captured main article contents. • Preprocessed text data for model training. • Tuned model outputs based on evaluation criteria. • Validated and reviewed summary accuracy and coverage. • Documented summarization workflow and outcomes.

I built a News Article Summarizer using Hugging Face Transformers, focusing on producing accurate and concise summaries. The process required data preprocessing, model fine-tuning, and validation of summary outputs. Quality assurance involved repeated review and adjustment to ensure summaries accurately captured main article contents. • Preprocessed text data for model training. • Tuned model outputs based on evaluation criteria. • Validated and reviewed summary accuracy and coverage. • Documented summarization workflow and outcomes.

2022 - 2023

Machine Learning Intern – Code Alpha

TextClassification
I developed classification models for disease prediction and credit scoring, involving annotation and validation of structured data. Tasks included cleaning datasets, assigning class labels, and verifying sample correctness before model training. Handwritten character recognition was also performed via annotated dataset preparation for CNN models. • Labeled text data and verified classification outputs. • Preprocessed and cleaned datasets for consistency. • Conducted manual validation of labeled samples. • Prepared structured input data for model training.

I developed classification models for disease prediction and credit scoring, involving annotation and validation of structured data. Tasks included cleaning datasets, assigning class labels, and verifying sample correctness before model training. Handwritten character recognition was also performed via annotated dataset preparation for CNN models. • Labeled text data and verified classification outputs. • Preprocessed and cleaned datasets for consistency. • Conducted manual validation of labeled samples. • Prepared structured input data for model training.

2021 - 2021

Education

I

Institute of Space Technology

Bachelor of Science, Electrical Engineering

Bachelor of Science
2015 - 2020

Work History

E

Embednexus

Machine Learning Intern

Houston
2024 - Present
H

Hex Softwares

Machine Learning Intern

Houston
2022 - 2023