Summary
Ahmad Anis is a Deep Learning Engineer with 7 years of experience building production-grade computer vision and multimodal AI systems, currently architecting a centralized LLM-driven video editing platform at Roll that automates clipping, transcript enhancement, bokeh effects, and dynamic VFX. He blends applied engineering with academic research—publishing at NeurIPS and ICLR and contributing as a Research Collaborator with the Data Provenance Initiative and an AI Research Fellow advised by Stanford faculty. Ahmad has delivered high-impact realtime solutions (e.g., reducing event editing from 48 to 1 hour via Active Speaker Detection) and optimized monocular depth and human matting for 2x performance gains in live streaming. His background spans diverse ML products from safety detection and neural search to RAG for medical vision-language models, and he actively mentors and leads community efforts at Cohere Labs Community and MESA. Notably, he pairs hands-on deployment experience with writing and public outreach—contributing technical tutorials to KDnuggets, cnvrg.io, and Medium—making him as effective at explaining complex ideas as at shipping them.
6 years of coding experience
2 years of employment as a software developer
Oxford Machine Learning Summer School OxML, Oxford Machine Learning Summer School OxML at University of Oxford
Bachelor's degree Computer Science, Bachelor's degree Computer Science at International Islamic University, Islamabad
English, Urdu