Simulation Platform & ML Engineer

GREEN14•Stockholm, ME

2d•Onsite

About The Position

At GREEN14, we are building a new generation of simulation technology for complex industrial processes. Our simulation team combines multiphysics models, machine learning and experimental process data to understand systems such as plasma reactors and high-temperature metallurgical processes. We can already build sophisticated models and fast surrogates. Now we want to turn that capability into robust software that can eventually be used beyond our own research environment. This requires a different kind of engineering. GREEN14 started five years ago with a mission to help Europe develop emission-free production of critical raw materials using hydrogen plasma. That journey gave us something increasingly valuable: our own experimental infrastructure. Today, we operate a full-scale plasma reactor in Stockholm. Alongside it, we have built capabilities in multiphysics simulation, internal solvers, AI and surrogate modelling. Our ambition is to connect these worlds: Experiment → physics → simulation → surrogate → optimization → experiment. But a great model sitting in a research notebook isn't a product. Models need data. Data needs pipelines. Experiments need to be reproducible. Software needs APIs. Models need to be versioned and tested. Computation needs to be fast and reliable. That is where you come in.

Requirements

Experience taking technically complex software from prototype to something other people can reliably use.
Python and modern software engineering practices
JAX or other numerical/ML frameworks
Production-grade backend architectures and APIs
Data pipelines, databases and modern data platforms
ML infrastructure and MLOps
Experiment tracking, model and version management and reproducible workflows
Testing, packaging, CI/CD and repository architecture
Performance optimization and computationally intensive workloads

Nice To Haves

Experience with Docker, cloud infrastructure, Kubernetes, GPU computing, FastAPI, MLflow, Weights & Biases, DVC or similar technologies
Experience deploying or optimizing JAX workloads, scientific computing or simulation software

Responsibilities

Design and implement robust backend architectures connecting sensor data, physics models, ML models and simulation workflows.
Turn JAX-based simulation and ML research code into efficient, maintainable and production-ready software.
Build pipelines connecting experimental and sensor data → model evaluation → fast simulation and surrogates → visualization and optimization.
Develop infrastructure for model training, checkpointing, evaluation, experiment tracking and reproducibility.
Structure repositories, APIs, packages and development workflows that can support a growing engineering organization rather than a single researcher.
Work with databases and data platforms capable of handling large volumes of simulation, experimental and model data.
Help establish testing, CI/CD, versioning and monitoring practices appropriate for scientific software.
Work with our simulation and AI engineers to identify computational bottlenecks and improve performance.
Contribute to deployment, cloud infrastructure and the architecture required to expose simulation capabilities to industrial users as our products mature.
Work closely with product and frontend engineering to turn highly complex computational capabilities into software that feels surprisingly simple to use.