Hamad Musa
Stanford,MT
Summary
Driven by adaptability and a deep expertise in Python, I spearheaded AI-driven safety solutions at Roadio.ai and significantly contributed to AI advancements at Spono. My work encompasses developing critical AI systems, enhancing software testing methodologies, and applying deep learning to real-world challenges, demonstrating a proven track record of innovative problem-solving and technical communication.
Overview
2
2
years of professional experience
Work History
Software Test Engineer
ROADIO.AI
San Francisco, CA
12.2024 - 01.2025
Developed AI-driven safety solutions for bicycles, mopeds, and motorcycles.
Conducted efficiency testing on over 100 software units prior to export.
Tested hardware and software efficiency of processors embedded with computer vision capabilities
AI/ML Software Development Intern
SPONO
San Francisco, CA
09.2023 - 12.2023
- Developed customized photographic profiles for party participants utilizing advanced deep learning techniques.
- Developed foundational AI systems enabling supply of photos/videos for events and concerts.
- Developed a full facial detection and recognition model used by the company using facial comparison and resolution enhancement
Computer Science Undergraduate Researcher
CURIS
Stanford, CA
06.2023 - 09.2023
- Chosen to collaborate with a PhD student in computer science during Summer 2023 under the mentorship of Parker Ruth
- Developed a wrist-worn device for non-invasive blood pressure measurement.
- Applied deep learning to estimate blood pressure from arterial width.
- Presented research findings to the Stanford Computer Science Department.
Education
B.S.E. - Computer Science
STANFORD UNIVERSITY
Palo Alto, CA12-2025
Skills
- Adaptability and Self-Learning
- Technical Communication
- Cross-disciplinary Collaboration
- Project Management
- Creative Problem Solving
- Python
- Deep Learning
- Automated and manual testing
- Diagnostic management
- Computer Vision
- C/C
- Natural Language Processing
- Neural Networks
- Machine Learning
- UI/UX
- BERT Language Models
Projects
BERT Language Model
Stanford, California (Jan 2024 – March 2024)
Developed and fine-tuned a BERT-based natural language processing model to enhance text understanding and classification. Adapted the model for three key downstream tasks:
● Sentiment Analysis – Identified the sentiment or emotion conveyed in a given sentence.
● Paraphrase Detection – Determined whether two sentences express the same meaning.
● Sentence Similarity – Measured the semantic similarity between sentence pairs.
Timeline
Software Test Engineer
ROADIO.AI
12.2024 - 01.2025
AI/ML Software Development Intern
SPONO
09.2023 - 12.2023
Computer Science Undergraduate Researcher
CURIS
06.2023 - 09.2023
B.S.E. - Computer Science
STANFORD UNIVERSITY
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