SouthwestX
5 days ago
CTO / Technical Co-Founder (f/m/d) role at startup METAKI
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Company information
- Company
- SouthwestX
- Location
- Dresden Germany
- Posted
- 5 days ago
Job description
**METAKI
**Dresden, Germany (TU Dresden) | On-site / Hybrid | Co-Founder / Equity | Deep Tech / AI for Engineering
**About Metaki
**Metaki is building a 'Large Engineering Model' (LEM): a graph-neural-network surrogate trained on large-scale finite element simulation (FEA) data that predicts, near-instantly, how complex 3D parts respond to different materials, loads and boundary conditions — replacing slow FEA runs. The initial focus is solid and structural mechanics, with the long-term ambition of a general foundation model for engineering physics.
**The opportunity
**Founded by Samir El Masri (Founder, ongoing PhD in FEM, 3 years of industry experience in FEM & AI/ML for engineering), Metaki already has a working FEM → data → NN pipeline, is building out a large 3D training dataset, and is running training alongside early industry discussions. What's missing is a technical co-founder to help scale the modeling and engineering work while Samir also drives funding applications and industrial pilot partnerships.
Tasks
**What you will own
**
**Model & platform development
**- Co-own the neural-network surrogate modeling pipeline (FEM data → GNN training → fast inference), extending it from solid/structural mechanics towards a general engineering-physics foundation model.
- Scale the large 3D simulation dataset and physics-informed training infrastructure currently in development.
**Technical leadership
**- Take technical/CTO ownership alongside Samir El Masri (Founder), covering architecture, ML engineering and data pipeline decisions.
- Help turn early industry discussions into validated industrial pilots by making the model production-ready.
**Fundraising support
**- Support funding applications and strategic introductions with a credible technical narrative and roadmap.
Requirements
**What you bring
**
**Core experience
**- Strong machine learning background, ideally with graph neural networks, surrogate modeling, or physics-informed ML.
- Comfort working with finite element simulation data and engineering/physics domain concepts (solid or structural mechanics a plus).
**Especially valuable
**- Software/ML engineering experience taking a research pipeline to a scalable, production-ready product.
- Interest in building toward a general foundation model for engineering physics.
- Experience with industrial pilot partnerships or applied research collaborations.
Benefits
**Why join Metaki
**- A working FEM → data → NN pipeline already built, not just an idea — real technical traction to build on.
- Early industry discussions already underway, with funding applications and pilot partnerships actively being pursued.
- Ground-floor technical co-founder equity in a company aiming to become a foundational-model platform for engineering physics.
Metaki welcomes co-founder candidates regardless of gender, age, ethnic origin, religion or belief, disability, sexual identity, or any other characteristic. This is a search for the right partner to build the company with, not a conventional hire.
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Required skills
- engineering
- ai
- software engineering
- machine learning
- architecture
- technical leadership
- equity
- ai/ml
- materials
- hybrid
- fea
- startup
- roadmap
- modeling
- loads
- co-founder
- on-site
- model development
- fem
- 3d parts
- gnn
- cto
- funding applications
- dresden
- platform development
- production-ready
- deep tech
- solid mechanics
- fundraising support
- structural mechanics
- ml engineering
- tu dresden
- physics-informed ml
- engineering physics
- foundation model
- scalable product
- lem
- technical narrative
- technical co-founder
- finite element simulation
- boundary conditions
- metaki
- large engineering model
- graph neural network
- surrogate model
- fea runs
- samir el masri
- nn pipeline
- 3d training dataset
- industrial pilot partnerships
- neural-network surrogate modeling pipeline
- fast inference
- 3d simulation dataset
- physics-informed training infrastructure
- cto ownership
- data pipeline decisions
- finite element simulation data
- engineering domain concepts
- applied research collaborations
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