For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

LoginCreate account
Invite to Job
M

Muhammad Besheer

AI Code Trainer & Evaluator | Outlier

EGYPT flag
6th of October, Egypt
$45.00/hrIntermediateLabelboxMercorOneforma

Key Skills

Software

LabelboxLabelbox
MercorMercor
OneFormaOneForma

Top Subject Matter

AI code evaluation
coding domains (Python, C, C++, embedded systems)
Rlhf Domain Expertise

Top Data Types

TextText
DocumentDocument
AudioAudio

Top Task Types

RLHF
Evaluation Rating
Transcription

Freelancer Overview

AI Code Trainer & Evaluator | Outlier. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, Helwan University (2021). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including RLHF, Evaluation, and Rating.

IntermediateEnglish

Labeling Experience

AI Code Reviewer & Arabic Language AI Tuner | Alligner

As an AI Code Reviewer & Arabic Language AI Tuner at Alligner, I reviewed AI-generated code for correctness, best practices, and safety, providing detailed structured feedback. I served as an Arabic language AI tuner, assessing model outputs for linguistic accuracy and dialect appropriateness. I identified and documented systematic failure modes in Arabic AI responses to improve dataset quality for fine-tuning. • Combined bilingual English/Arabic proficiency to evaluate language and coding tasks. • Performed dataset quality control for Arabic NLP fine-tuning. • Focused on both technical code review and linguistic/cultural relevance in model outputs. • Used internal/proprietary tooling for annotation and review.

As an AI Code Reviewer & Arabic Language AI Tuner at Alligner, I reviewed AI-generated code for correctness, best practices, and safety, providing detailed structured feedback. I served as an Arabic language AI tuner, assessing model outputs for linguistic accuracy and dialect appropriateness. I identified and documented systematic failure modes in Arabic AI responses to improve dataset quality for fine-tuning. • Combined bilingual English/Arabic proficiency to evaluate language and coding tasks. • Performed dataset quality control for Arabic NLP fine-tuning. • Focused on both technical code review and linguistic/cultural relevance in model outputs. • Used internal/proprietary tooling for annotation and review.

2023 - Present

AI Code Trainer & Evaluator | Outlier

RLHF
As an AI Code Trainer & Evaluator at Outlier, I evaluated and ranked AI-generated code responses for accuracy, efficiency, and code quality. I annotated large language model outputs using structured evaluation frameworks for prompt quality, response ranking, and behavioral tagging. I contributed to reinforcement learning from human feedback (RLHF) pipelines by providing expert-level feedback that drove improvements in model training signals. • Assessed model responses in Python and C/C++ across diverse coding domains including scientific computing and ML libraries. • Tagged behavioral weaknesses such as hallucination, over-engineering, and instruction drift in AI outputs. • Applied expertise in embedded C and systems programming to low-level code evaluations. • Utilized internal/proprietary tooling and frameworks for annotation and evaluation tasks.

As an AI Code Trainer & Evaluator at Outlier, I evaluated and ranked AI-generated code responses for accuracy, efficiency, and code quality. I annotated large language model outputs using structured evaluation frameworks for prompt quality, response ranking, and behavioral tagging. I contributed to reinforcement learning from human feedback (RLHF) pipelines by providing expert-level feedback that drove improvements in model training signals. • Assessed model responses in Python and C/C++ across diverse coding domains including scientific computing and ML libraries. • Tagged behavioral weaknesses such as hallucination, over-engineering, and instruction drift in AI outputs. • Applied expertise in embedded C and systems programming to low-level code evaluations. • Utilized internal/proprietary tooling and frameworks for annotation and evaluation tasks.

2023 - Present

Education

H

Helwan University

Bachelor of Science, Electronics and Communication Engineering

Bachelor of Science
2016 - 2021

Work History

V

Valeo

Embedded Software Engineer

6th of October
2022 - Present
I

ITI

Embedded C Instructor

Giza
2023 - 2023