I lead 150 engineers across Canada, Serbia & China, building GPU virtualization software for AMD's Instinct AI accelerators. I move with urgency, and I run on AI. We're powering the next generation of AI - and defining how to get the most out of it.
I'm a Senior Director at AMD, leading cloud GPU software development for the Instinct and MI-series AI accelerator line. My teams own the driver stack that enables GPU virtualization across KVM, Hyper-V, and VMware - the software layer that lets hyperscalers and enterprises partition and run the world's most powerful AI hardware reliably at cloud scale.
I lead 150 engineers across Canada, Serbia, and China - and I'm pressing them to adopt AI ahead of the industry, out at the edge where the playbook hasn't been written yet. Test infrastructure rebuilt for the age of AI-generated software. Bug turnaround falling and feature throughput climbing, year over year. My job is to build the environment, direction, culture, and people that make that pace sustainable.
I graduated from the University of Waterloo in Computer Engineering. I think entrepreneurially. I believe the gap between where AI is today and where it's going in five years is the biggest opportunity of our professional lives - and I'm not willing to watch from the sideline.
Three countries, thirteen time zones, one direction. Leading at this scale means building the culture, the process, and the technical bar simultaneously - and making sure AI is embedded in how every team works.
KVM, Hyper-V, VMware. Each platform is a different world. Shipping reliable, performant GPU virtualization support across all three - for the most in-demand AI silicon on earth - requires zero margin for error.
GPU virtualization sits at the exact intersection of AI infrastructure, cloud economics, and semiconductor dominance. The next decade of computing is being built on this stack. Right now.
Accountable for the driver stack that lets cloud providers deploy and partition Instinct MI-series GPUs at scale - the foundational software layer for how the world consumes AI compute. It's also the proving ground for everything I write about: an engineering organization going AI-native in production, not in theory.
Led the Windows Software Platform Group: the kernel-mode driver and the full software stack delivered to Microsoft for Windows operating systems running AMD GPUs. Built and scaled the teams, drove platform strategy, and developed the operational muscle to run distributed engineering organizations across time zones, cultures, and technical domains.
One of the most rigorous engineering programs in North America, paired with a co-op model that means graduating with years of real industry experience. Computer Engineering built the technical depth to understand what teams are building. The Management Science minor added the second lens early: not just how to build, but which problems are worth the resources.
Apex Dynamics gives up on "solving with AI" by hiring a consultant to tell them what they should already know.
Read →Tokens aren't a cost problem. They're a guidance problem. The winners won't have the lowest AI bill - they'll generate the most value per token.
Read →Not seeing the gains and spending too much on tokens are the same problem. Stop applying AI everywhere. Aim the tokens where the value is.
Read →This was a prediction. Now it's happening. What it means for engineers, teams, and companies who want to still be relevant next year. A call to action.
Read →Pilots everywhere, gains nowhere. ICs accelerate tasks - only senior leaders can redesign how work flows across teams. You can't redesign what you don't understand.
Read →
I got here by iterating. OpenClaw - an open-source autonomous agent powered by Claude - ran my calendar, my inbox, and my daily workflow for months. It proved what agentic AI could do. It also cost me my evenings: endless tinkering just to keep it working. Claude Code flipped that ratio - the same agentic power, in the terminal, and it just gets things done. This site included. Cursor is my daily driver at work, and now I'm running it at home too.
The tools will keep changing. The principle won't: aim the tokens where the value is. AI-native doesn't mean AI everywhere - it means AI integrated into how the work actually flows, where it moves the outcomes that matter.
If you're building at the infrastructure layer, thinking seriously about AI adoption, or working on something that matters - I want to hear about it. Based in the Greater Toronto Area.
mario@filipas.com