Machine learning engineer • spreitenbach
Deep learning on 3D CAD data where correctness translates into steel and aluminum, not pixels.The ultimate tinkerer's playground.Physical machines, shop floors, and modern cloud architectures all i... Mostra di più
European engineering and operational consulting group founded in 2005 and specialized in the industry.We work in the Pharma, Biotechnology, Medical Devices, Energy, Infrastructure, Transportation, ... Mostra di più
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Implementing & Evaluating new ML Models for Geometry Generation.In this internship, you will have the opportunity to review, implement, and evaluate the latest machine learning models in the realm ... Mostra di più
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GlobalEngineer is an innovative company in several engineering fields that is active in.To strengthen our team, we are looking for an interested and motivated RAM Engineer with immediate effect.Ens... Mostra di più
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Geometry with consequences. Deep learning on 3D CAD data where correctness translates into steel and aluminum, not pixels.
The ultimate tinkerer's playground. Physical machines, shop floors, and modern cloud architectures all in one stack.
Tangible impact. Your work feeds the data pipelines that power our AI quoting and calculation engines. Factories operate differently because of what you ship.
High autonomy. A fragmented industry full of unsolved problems, and the freedom to prototype, test, and deploy your own solutions end-to-end.
What you'll do:
Develop advanced feature-extraction and data-processing solutions for 3D CAD, 2D, and batch data feeding our machine learning systems.
Build, train, and industrialize deep learning models that understand geometry and drive our quoting, calculation, and optimization engines.
Enhance and extend our 2D/3D/CAD tools and geometry solutions.
Design, engineer, and own cloud services, APIs, and automated model training/deployment — from proof of concept to production under real load.
Go deep across interconnected cloud, data, computer vision, and ML services spanning multiple languages and platforms.
For over 10 years at Imnoo at Imnoo, we are bridging the gap between applied artificial intelligence and heavy industrial machinery.
Imnoo is sustainability focused, creating real value as well as a profitable company with stable base.
We aren't just riding the recent AI wave, we have been deeply rooted in machine learning and statistical analysis for 10 years, delivering real, measurable value to a stable, loyal client base of manufacturers.
Imnoo is a leading SaaS platform built to analyze CNC parts, automate quoting and planning, and seamlessly connect the cloud with real manufacturers and shop-floor machines. We are looking for a unique hybrid (a CAD AI IoT Engineer / Tinkerer) who thrives at the intersection of software architecture and physical hardware. You won't just be writing code behind a desk; you will be the mastermind connecting our battle-tested algorithms directly to the pulse of the factory floor. If you love reverse-engineering industrial protocols, building edge data pipelines, and parsing complex 3D geometry to automate the business of manufacturing, this is your playground.
Machine learning / deep learning experience. Real development experience or serious projects: building, training, and shipping models (PyTorch, PyTorch Lightning, scikit-learn or similar), not just coursework or library calls.
Strong mathematical understanding. Linear algebra, geometry, statistics, probability. Our problems are mathematical at their core; this is a real requirement, not a nice-to-have.
Strong software engineering. 3+ years across dynamically and statically typed languages (e.g. Python plus C++/C#), with sharp problem-solving skills.
Computer Graphics / Game development / 3D foundations. Hands-on experience in game development, 3D geometry, rendering, fluid simulations, CUDA/GPU programming, or similar. This is a headline requirement, not a bonus.
Production experience. You've built or maintained systems running under heavy load.
Nice to have
CAD system development experience; CNC / mechanical industry exposure.
ML industrialization: ONNX, quantization, Docker, MLOps practices, automated model training and deployment.
Large-dataset work: processing, clustering, filtering, indexing, and querying; statistical testing and outlier analysis.
Cloud services (AWS/Azure), SQL and NoSQL databases, full-stack experience (e.g. Angular).
