PRODUCT LEADERSHIP × MACHINE LEARNING

I ship products.
I train models.

Rasmus Rasmussen — product leader with 25+ years in games and tech, now building small language models, mixture-of-experts architectures, and the data tooling behind them.

● OPEN TO WORK
Rasmus Rasmussen
rasmus_rasmussen.rawSeattle, WA
YEARS IN TECH & GAMES
25+
YEARS IN LEADERSHIP
10
MODELS PUBLISHED
190+
OPEN DATASETS
115+
CITED IN RESEARCH
arXiv

01/Selected work

deep dives →
CITED IN RESEARCH

My ReWiz-Llama-3.2-3B model was used in “Model Merging Scaling Laws in Large Language Models” (arXiv:2509.24244).

02/Track record

// the U.S. chapter — 2005 onward

SHIPPED: Forza Motorsport 8 · Forza Horizon 5 · DOTA 2 · Rise of the Tomb Raider · Sunset Overdrive · Forza Motorsport 5 · Minecraft (Xbox) · Age of Empires 2 (Xbox) · Above the Fold · Torgar's Quest ·  

03/Research velocity

Most of my public catalog is deliberate volume: small, cheap experiments run fast, in series, with the misses left visible. That cadence is how the serious work gets found, whether that's the MoE builds, the data tooling, or the models that end up cited. The through-line is squeezing more out of small models, for edge devices and agentic workflows, where I think the next few years of practical AI get decided. Simulations are what bridged games and AI for me, and I still build ecology sims for fun.

MODEL SERIES ON HUGGING FACE

ReWiz MoE 1B Experts TiTan Databird VanRossum CleverBoi Hemispheres

190+ models · 115+ datasets · 12 curated collections

04/Writing

all posts →

rasmus@ras.ms:~$ ./get_in_touch

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Product & program leadership for AI startups — from model strategy to shipped.