AI / ML Research Engineer

I work at the intersection of machine learning, research, and systems.
My primary interest is understanding why models work, when they fail, and how to make them more reliable , not just training them to get a number.

I enjoy:

• Building ML systems grounded in theory
• Exploring physics-informed ML, kernels, optimization, and LLM internals
• Writing code that is readable, reproducible, and explainable

• NLP, Kernel methods, optimization, and representation learning
• Large Language Models (training, inference, KV cache, evaluation)
• Research-grade experiments & ablations (not just demos)

Languages: Python, C++, JavaScript
ML / DS: PyTorch, NumPy, SciPy, scikit-learn
Systems & Tools: Linux, Git, Docker (learning deeper systems internals)
Research: Experiment design, ablation studies, paper reproduction

• Understanding failure modes of ML models
• Avoiding “black-box worship”
• Bridging theory ↔ implementation
• Long-term: contributing to research-driven ML teams

• Data Science Roles
• Research internships (AI / ML / Applied Research)
• Open-source collaboration
• Technical discussions & paper reading groups

“Strong models are built by understanding their weaknesses.”