ML Engineer – MLOps & Platform Engineering (m/w/d)

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.
Powerful models are only as good as the data they learn from, and only matter once they ship. That's where you come in. You'll help treat training data as a first-class asset: versioned, traceable, and continuously improved, with its impact on results made visible. You'll build the pipelines, the model lifecycle, and the live AWS service that carry our models from experiment to customers. The systems around the models are as much the product as the models themselves.
You can work from our office in Würselen near Aachen or remotely from anywhere in Germany, with occasional travel for team events and on-sites.
What You Will Do
You help build the platform behind our AI Solver and grow into owning it: the systems that manage our training data and models, bring them reliably into production, and serve them to customers.
Build the training and data platform. Design the pipelines and systems that version, track, and manage our training data and models as the assets they are, with reproducibility and lineage built in.
Build the model lifecycle. Create the path from experiment to production: model versioning, a registry, promotion, and repeatable training and deployment.
Close the loop to production. Set up monitoring that surfaces model degradation and flags when incoming data drifts outside what a model handles well, so our AI engineers know where to act.
Enable the AI team. Provide the workflows and tooling our AI engineers and data scientists use to train, evaluate, and deploy models. You build the rails, they drive.
Run and evolve the production service. Operate and scale our AWS service that serves the AI models, keep it fast and reliable, and extend it as we grow, for example from serving a single model to multiple selectable models, including access-controlled or user-specific ones.
Work hand in hand with the Cloud team. They build our simulation platform and are the main consumer of your AI service, so shipping new capabilities means designing the interface and rollout together.
Pitch in where it counts. We're a small team, so the platform work reaches into classic software and infrastructure engineering. You'll have room to follow the problem wherever it leads.
This role builds and runs the platform. Assessing model quality, curating training data, and the modeling itself sit with our AI engineers and data scientists. Your job is to make their work fast, reproducible, and production-ready.
Your profile
Background in Computer Science, Data Engineering, Machine Learning, or a related field, with 3+ years of relevant experience in industry or research.
Strong Python skills and solid software engineering fundamentals (testing, version control, CI/CD).
Experience bringing software services or data pipelines into production. Experience with ML systems in production is a strong plus.
Experience with a cloud environment (AWS, Azure, or GCP) and with containerization using Docker.
Pragmatic and reliability-minded. You focus on building systems that work and keep working.
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
Exposure to scientific computing, simulation data or HPC environments.
Experiment tracking and model or data versioning tools (for example MLflow, Weights & Biases, or DVC).
Workflow orchestration (Airflow, Prefect, or similar).
Deploying AI models beyond the cloud: CPU-only on-premises or edge targets, and hybrid setups.
Inference optimization (quantization, pruning, efficient architectures).
AWS stack (S3, EC2, ECR, SageMaker) and infrastructure as code (Terraform).
Building internal platforms or tooling that other engineers build on.
You won't check every box. If you know your gaps and how to close them, apply.
What we offer
A real technical challenge. You're reshaping a proven simulation engine for a market moving to cloud and AI.
Room to grow. You start by building core parts of the platform and take on more ownership as the platform and the team grow.
Ownership and impact. About 40 people. Your decisions shape the product and the business.
Flexible work. Remote from anywhere in Germany or hybrid at our office in Würselen near Aachen.
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.

Work arrangement
Yes