[Hiring] ML Engineer – AI Solver Development @SIMCON

Role Description

For decades, Cadmould has been one of the fastest and most advanced injection molding simulators on the market, trusted across the plastics industry. Then we built something the field had never seen: Cadmould AI Solver, the first Large Engineering Model (LEM) for plastic injection molding, a transformer-based neural physics model that delivers high-fidelity results up to 1,000x faster than classical solvers. It turns simulation from a slow validation step into something engineers can explore in real time.

This role can be performed from our office in Würselen near Aachen or remotely from Germany, with occasional travel for team events and on-sites.

What You Will Do

• Work alongside our research engineers and domain experts to advance our AI Solver: the model, training, datasets, and evaluation that capture the complexity of real-world injection molding.
• Improve the model by getting to know our transformer-based AI Solver inside out: its architecture, training strategies, data generation, and design trade-offs. Then iterate on architecture, training objectives, and learning strategies to push accuracy and generalization.
• Run and analyze experiments by designing experiments, analyzing model behavior, documenting findings, and translating them into concrete improvements to data, training, and model quality.
• Build shared understanding by communicating results clearly and contributing to reproducible workflows and shared technical context across the team.
• Strengthen the platform by extending our training, evaluation, and deployment workflows and infrastructure.
• Pitch in beyond the model as a small, fast-moving team, stepping outside our lane when it counts, from the AI service side to agentic AI features for product experiments.

Qualifications

• Background in Machine Learning, Physics, (Computational) Engineering, Applied Mathematics, or a related field, with 3+ years of relevant experience, or a strong foundation from personal or research projects.
• Solid, practical machine learning fundamentals with PyTorch, TensorFlow, or similar, plus the usual data-wrangling stack (NumPy and friends).
• Ability to turn research papers into working code and a desire to understand model behavior.
• Structured and methodical in work, documenting experiments, keeping work reproducible, and explaining findings clearly.
• Experience with coding agents as a system to optimize, not just a gadget.
• Fluency in English; German is a plus.

Requirements

• Nice to have: Transformers or sequence-based models.
• Geometric deep learning, GNNs, or learning on structured 3D data (point clouds, meshes).
• Scientific machine learning, surrogate modeling, or physics-based data.
• Cloud environments for machine learning workloads (training, experimentation, deployment).
• Experience with experiment tracking and versioning tools (MLflow, Weights & Biases, DVC), plus solid software engineering fundamentals (testing, version control, containerization).
• You won't check every box. If you know your gaps and how to close them, apply.

Benefits

• A real technical challenge: reshaping a proven simulation engine for a market moving to cloud and AI.
• Ownership and impact: about 40 people, where your decisions shape the product and the business.
• Modern tooling: Notion, GitHub, Linear, coding agents.
• Direct and honest culture: candid feedback is standard practice, both internally and externally.
• No micromanagement.