Amaan Abubakar

Software Engineer Intern @ Valearnis

About

I am a Computer Science and Applied Mathematics student who likes building simple tools for complex, real-world problems.My interests are in software, data, and decision-making. I enjoy creating products that are not just functional, but clear and easy to use. I pay close attention to the small details, from user experience to how the frontend and backend work together.I am especially interested in opportunities where I can use technical skills to make a real impact.

Country

United States

City

New York City Metropolitan Area

Industry

Computer Software

Skill

React.js, Git, HTML, Java, Python (Programming Language), Technical Communication, Financial Analysis

Experience

Software Engineer Intern

Valearnis

2026-1 - Present · 8 mos

Perth, Western Australia, Australia

- Architected and deployed an AI-powered content generation pipeline using Django, PostgreSQL, and large language model integrations to automate lesson asset creation, reducing manual content production time by 60%. - Diagnosed and resolved cross-device mobile styling issues by auditing responsive breakpoints and restructuring layout components, reducing user-reported interface bugs by 70% and improving mobile engagement metrics. - Optimized database queries and introduced caching strategies for high-traffic endpoints, improving API response times by 40% and supporting scalable user growth without infrastructure changes.

Founder

Curio Learning

2025-5 - Present · 1 yr 4 mos

New York, United States

- Engineered an AI-powered platform enabling children to watch YouTube videos and receive dynamically generated educational quizzes, boosting engagement in test groups by 40%. - Designed branding and product roadmap, attracting 50+ early adopters and positioning Curio as a scalable edtech solution for interactive learning.

Artificial Intelligence Analyst

Outlier

2024-10 - Present · 1 yr 11 mos

- Contributed to artificial intelligence model improvement through structured coding and evaluation tasks, maintaining 97% accuracy against quality rubrics and specification requirements. - Strengthened model reliability by identifying failure modes, correcting logical inconsistencies, and refining computational reasoning, reducing recurring errors by 40% across assigned task sets.