Filip Makraduli
Profile
Founding Member of Technical Staff at Superlinked.
Biography
Filip Makraduli is an applied AI researcher and founding ML Developer Relations engineer at Superlinked, where he designs and ships small‑LLM inference systems for search, retrieval, and agents in production. He holds a master’s degree in Biomedical Data Science from Imperial College London. Before Superlinked, Filip worked in machine learning, data science, and developer relations roles across early‑stage AI startups and larger enterprises, building language understanding, retrieval‑augmented generation (RAG), and LLM pipeline tooling while partnering closely with product and platform teams. He is a frequent open‑source contributor, with contributions to kernel libraries, model‑inference providers, and hands‑on demos used by practitioners. Filip is a co‑author of several publications on efficient transformer architectures and inference, including work on faster normalization for LLMs. He is an experienced speaker at meetups and conferences such as AI Engineer Europe and Berlin Buzzwords, sharing practical lessons on efficient transformers, retrieval systems, and embedding inference for production AI teams.
Conference Sessions
- 2026 06 29 filip makraduli turning my obsidian vault into a local ai engineer — Turning My Obsidian Vault Into a Local AI Engineer (2026-06-29, 1:15pm-2:15pm)
- 2026 07 01 filip makraduli weight folding cuda streams and the bug that made my model speak backwards — Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards (2026-07-01, 3:45pm-4:05pm)
Evidence Graph
This evidence graph summarizes how this person appears across the conference source graph: scheduled sessions, linked videos, transcripts, and slide-derived evidence.
Linked Sessions
- Turning My Obsidian Vault Into a Local AI Engineer
- Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards
Media Signals
youtube-qdh_x-uRs9g— 5 slide-derived text signals- Slide-derived themes for
youtube-qdh_x-uRs9g: makes, embedding, learning, first, principles, models, production, best. - Evidence links for
youtube-qdh_x-uRs9g: youtube qdh_x uRs9g, youtube qdh_x uRs9g slides, youtube qdh_x uRs9g reconstructed slides