Karim Shaban

Karim Shaban has built perception systems and large-scale data pipelines for autonomy, after cutting his teeth on projects ranging from humanoid robots and indoor reconstruction to facial reenactment and self-driving racing cars (starting with his Master’s in Munich). Based in London, he now leads Perception at Humanoid, coming from six years in self-driving cars spanning startups and major teams including Lyft’s autonomous division and Toyota.

Artem Shutak

With 14 years as a software engineer, architect, and lead, Artem moved from full-stack and JVM work at Oracle into a decade of data engineering, scaling Europe’s largest social platforms and building global data platforms. His track record spans 100s of petabytes across data lake/DWH architectures and streaming pipelines processing millions of events per second across ads, retail media, e-commerce, social, and banking.

Giulio Cerruti

Over the past 10 years, Giulio has led R&D across consumer and medical applications, working on mobile manipulators, legged robots, and exoskeletons, and earning a PhD on the design and control of five-finger robotic hands. One of the first employees at Humanoid, he now heads Controls, coming from a Platform Team Lead role at Dyson Robotics Research where he developed a new household-robot form factor.

Fredrik Lie Larsen

Fredrik joined Humanoid as a mechanical engineer working on the head, after 3.5 years at 1X Technologies where he engineered multiple joints on NEO and helped scale the team.

Diogo Almeida

Diogo has a decade of experience in robotic manipulators and holds a PhD in Computer Science focused on dual-arm manipulation systems. Most recently, he spent five years at Ocado Technology developing robotic packing systems, taking a new product from research prototype to production.

Cody Griffin

A systems engineer spanning robotics, distributed storage, networking, and low-latency systems, Cody most recently worked at Cloudflare on exabyte-scale object storage. Cody is drawn to solving hard problems under real-world constraints and building systems that stay fast and reliable at scale.