Marvel Fusionmarvelfusion.com
Research Software Engineer - Scientific Computing & GPU Infrastructure
- Location
- Munich
- Remote
- On-site
- Employment type
- Full-time
- Experience
- Mid-Level
- Field
- Engineering
Requirements at a glance
- Degree in STEM (Required)
- Python (Required)
- GPU computing frameworks (Required)
- Linux server deployment (Required)
- English (Required)
- AWS (Preferred)
- CUDA (Preferred)
RequiredPreferred
Today
Research Software Engineer - Scientific Computing & GPU Infrastructure
Munich
Who we are
Founded in 2019, Marvel Fusion is Europe’s leading fusion energy company, uniting 120 scientists, engineers, and entrepreneurs across our locations in Munich and Colorado.
Backed by over €385 million in public and private funding, we’re driven by a shared mission: to deliver clean, abundant energy to the world.
Why Marvel Fusion
By joining us, you will be:
- Solving one of the most complex technological challenges known to humanity, harnessing Fusion on Earth
- Part of a highly purpose-driven team working on providing the world with clean, safe and abundant energy
- Working alongside world-leading scientists and entrepreneurs in the field of Fusion
- Part of a start-up where growth on a company and individual level is the default
The impact you will make
As part of our multidisciplinary research and engineering team, you will shape how we tackle computational challenges in fusion research. You will combine your software expertise with the team’s knowledge of physics and engineering to develop new approaches, make technical decisions and turn ideas into reliable scientific software. From advancing numerical performance to enabling shared GPU computing on NVIDIA servers or AWS, you will own meaningful technical challenges and contribute directly to our scientific progress.
Your responsibilities
- Develop and maintain scientific Python software, turning research requirements into reusable, tested features and clear interfaces.
- Work with scientists to define acceptance criteria and verify numerical results against reference cases and agreed accuracy requirements.
- Use profiling to identify bottlenecks; compare algorithms, code translations and CPU/GPU implementations through reproducible benchmarks.
- Prepare and deploy scientific workloads on NVIDIA GPU servers or AWS using reproducible environments and automated execution workflows.
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