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Founding Engineer 3D Computer Vision

Pubblicato il 02-10-2026 - SE3Labs in Milano

About SE3
SE3 Labs builds Spatial AI by combining 3D computer vision and large language models (LLMs). Ultimately, our AI will solve the spatial Turing test: It will be able to answer any question about 3D data that a human analyst could answer. Spatial AI will unlock tremendous value in industries like construction, smart infrastructure, smart factories, and defense. As our long-term vision, our spatial AI is a crucial building block for enabling LLM-controlled humanoid robots.
Se pensa di essere il profilo ideale per la seguente occasione, si candidi dopo aver letto la descrizione completa.
- Our team consists of computer vision experts and repeat founders, who have worked with Elon Musk at Tesla and at Nvidia, KPMG, Bosch, and Artisense. Daniel is the president of the European Computer Vision Association.
- Within our first year, we have secured mid-six-digit revenue and aim for seven-digits in 2025.
- We are a dual-use tech company selling to civil customers and the German armed forces. We will expand internationally in the next six months.




- We just raised a heavily over-subscribed financing round from Europe's best VCs (Midas List Europe) and world-class angel investors (unicorn founders, Sequoia scout, Princeton professor, etc.). We are part of EWOR.

About the Role
As the Founding Engineer 3D Computer Vision & AI, you will work closely with Lukas (CEO) and Simon (CTO) to build an AI-first perception and robotics company, similar to Tesla's Autopilot Team.
What you might work on
- Develop and train new network architectures
- Optimize network deployment on Nvidia Jetsons
- Build our data engine using local and cloud compute
- Combine existing LLMs with 3D computer vision to improve SpatialGPT
- Fine-tune LLMs for better performance on 3D-related tasks
- Integrate networks into real-time systems on drones

What we are looking for
- Strong general software engineering skills
- Theoretical and practical experience with deep learning
- Theoretical and practical

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