ML Engineer – AI Solver Development (m/w/d) at SIMCON

Würselen, North Rhine-Westphalia, Germany -

Full Time

Start Date

Immediate

Expiry Date

05 Oct, 26

Salary

0.0

Posted On

07 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

PyTorch, TensorFlow, NumPy, Transformer Architecture, Geometric Deep Learning, GNNs, Scientific Machine Learning, Surrogate Modeling, MLflow, Weights & Biases, DVC, Containerization, Version Control, Python, Data Wrangling, Physics-based Modeling

Industry

Software Development

Description

Ihre Aufgaben The Role 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 is 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. It's live as a research preview on our site, and it has already shipped to our first customers. Teaching a neural network the physics of molten plastic is uncharted territory, and we're still early. You'll work at the core of the model itself: how it's built, how it learns, and the data it learns from. Few teams anywhere are building engineering models like this, and the design space is wide open. 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 You 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. Get 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. Design experiments, analyze model behavior, document findings, and translate them into concrete improvements to data, training, and model quality. Build shared understanding. Communicate results clearly and contribute to reproducible workflows and shared technical context across the team. Strengthen the platform. Extend our training, evaluation, and deployment workflows and infrastructure. Pitch in beyond the model. As a small, fast-moving team, we step outside our lane when it counts, from the AI service side to agentic AI features for product experiments. You'll have room to follow the problem wherever it leads. Ihr Profil 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. We're hiring at mid to senior level. Solid, practical machine learning fundamentals with PyTorch, TensorFlow, or similar, plus the usual data-wrangling stack (NumPy and friends). You turn research papers into working code, and you're not satisfied until you understand why a model behaves the way it does. Structured and methodical in how you work. You document experiments, keep your work reproducible, and explain findings clearly. Coding agents are part of how you build, and you treat them as a system to optimize, not a gadget you occasionally reach for. You keep sharpening how you work with them, from context and tooling to workflow, and you know exactly where they help and where they get in the way. English is our working language and all you need to do the job. German is a plus. We're still a mostly German-speaking culture shifting toward English. 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). 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. Warum wir? A real technical challenge. You're reshaping a proven simulation engine for a market moving to cloud and AI. Ownership and impact. About 40 people. Your decisions shape the product and the business. Modern tooling. Notion, GitHub, Linear, coding agents. We're building the practices that make this work, and you help shape them. Direct and honest culture. Candid feedback is standard practice for us, both internally and externally. No micromanagement.

Responsibilities

Advance the AI Solver by iterating on transformer-based architectures, training strategies, and datasets to improve accuracy and generalization. Design and analyze experiments to translate findings into concrete improvements for the neural physics model.