Bhargav Bhavsar

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Text Generation

Emotion Recognition

Object Detection

Data Collection

Freelancer Overview

I have a strong background in quality assurance and data-driven process improvement, with hands-on experience in precise measurement, inspection, and reporting. My work as a Quality Laboratory Technician involved creating and executing CMM programs, generating detailed inspection and SPC reports, and ensuring the accuracy and integrity of manufacturing data using tools like Zeiss Calypso, Zeiss PiWeb, and Gagetrak. I am skilled in interpreting complex engineering drawings, performing root cause analysis, and applying lean methodologies to improve processes. My experience in preparing documentation, conducting audits, and maintaining meticulous records has given me a keen eye for detail and accuracy—skills that are crucial for data labeling, annotation, and ensuring high-quality AI training data. I am adept at using Microsoft Excel and other software for data analysis, and I am committed to delivering reliable and structured datasets for machine learning and AI applications.

Labeling Experience

AI Trainer

Scale AIImageText GenerationEmotion Recognition

Scope: dvanced reasoning domains: Mathematics, scientific analysis, legal reasoning, software engineering, and other technical fields requiring deep expertise Model alignment work: Training frontier AI models to be more helpful, accurate, and safe through reinforcement learning from human feedback (RLHF) Safety and quality assurance: Identifying hallucinations, biases, and potential risks in model outputs Multi-step problem solving: Tasks requiring chain-of-thought evaluation and verification across complex workflows Tasks: Evaluating and ranking model responses for technical accuracy and clarity Writing detailed, expert-level explanations for complex topics Red-teaming models to identify failure modes and safety concerns Annotating reasoning chains and identifying logical errors Creating high-quality prompt/response pairs for specialized domains Multi-turn conversation evaluation and refinement

Scope: dvanced reasoning domains: Mathematics, scientific analysis, legal reasoning, software engineering, and other technical fields requiring deep expertise Model alignment work: Training frontier AI models to be more helpful, accurate, and safe through reinforcement learning from human feedback (RLHF) Safety and quality assurance: Identifying hallucinations, biases, and potential risks in model outputs Multi-step problem solving: Tasks requiring chain-of-thought evaluation and verification across complex workflows Tasks: Evaluating and ranking model responses for technical accuracy and clarity Writing detailed, expert-level explanations for complex topics Red-teaming models to identify failure modes and safety concerns Annotating reasoning chains and identifying logical errors Creating high-quality prompt/response pairs for specialized domains Multi-turn conversation evaluation and refinement

2025 - 2025

Education

Work History