A career in chapters
From scientific code to production AI.
The timeline matters because the disciplines kept stacking: scientific computing became signal processing, signal processing became ML, and software engineering became the delivery layer.
Engineer · Programmer · Technical Founder
I build AI systemsthat meet the real world.
Software engineer working across AI, computer vision, signal processing, mobile, embedded systems and scientific computing — with a long-standing habit of turning difficult ideas into working products.
Based in North Vancouver, BC
Canada
30+years engineering
IntelVaunt smart glasses
PatentAI-based color sensing
120+customers at Golaks
7+ yrsscience & AI on YouTube
Selected work
Things I actually built.
A mix of production software, experiments and engineering projects. The common thread is simple: theory is useful when it survives contact with hardware, data and users.
Current work · sysHuman
AI products, not just AI demos.
A separate home for the products built through sysHuman. Each item below links directly to the live product, so the portfolio stays connected to something you can actually open and use.
“
The interesting part of engineering is the distance between an idea and a system that actually works.Kadir Ertürk · notes from the workbench
Learn with Kadir
AI from the mathematics up.
The YouTube channel is a second part of the work: long-form explanations of AI, mathematics, signal processing and science, mostly in Turkish with some English.
01
Deep learning & transformers
Attention, self-attention, Q/K/V, embeddings, backpropagation and neural networks — with the math left visible instead of hidden.
Browse YouTube ↗
02
Mathematics for ML
Matrices, eigenvalues, differential equations, calculus and the mathematical pieces that make models less mysterious.
03
Signal processing
Fourier transforms, vibration data, FFT and real-time DSP — the engineering layer beneath many “AI” problems.
04
Science & experiments
Cell biology, sensors, code-on-DNA ideas and other experiments driven by curiosity rather than a product roadmap.
About
A programmer with an engineer’s reflex.
I started writing scientific software at Istanbul Technical University in the late 1980s. Over the decades the tools changed, but the pattern stayed: understand the system, make the model concrete, then ship it.
That path has included industrial automation, entrepreneurship, mobile and backend development, machine learning, Intel’s Vaunt wearable project, BLE/IoT systems and — today — generative AI products through sysHuman.
Kadir Ertürk
Working areas
AI / MLLLM · CV · RAG · Transformers · SVM · LSTM
SoftwareJava · Python · PHP · REST · Web
Mobile / EdgeAndroid · iOS · BLE · IoT
ScientificDSP · Fourier · Signal processing · Image processing
EmbeddedESP32 · C/C++ · Sensors · RF
Elsewhere
The work lives in more than one place.
Professional history, long-form teaching, experiments and company work each have their own home.