Senior Software Engineer
Job description
About the role
The role centers on cloud operations and distributed computing to support stellarator design teams. It focuses on building and scaling software infrastructure for hyper-dimensional design exploration.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
General cloud infrastructure is managed so that infrastructure as code, permission management, and cloud efficiency keep systems secure, aligned with workload demands, and cost-effective. This foundation supports reliable workflows for teams across the company.
Cloud computing is democratized through Kubernetes, Ray, Dask, Docker, and PEX, enabling more teams to run simulations and analysis safely at scale. Access expands for complex workloads across the organization.
The development experience is unified and streamlined using CI/CD, Devcontainers, and monorepos, reducing friction when multiple contributors work on the same systems. Consistent tooling helps diverse teams collaborate on shared platforms.
Observability is improved across software and infrastructure so performance, reliability, and failures are understood quickly. Better visibility enables faster troubleshooting and data-driven decisions for the teams.
Scientists and engineers rely on these clusters to complete timely, large-scale simulations.
DevOps practices are strengthened and DORA metrics are optimized to track deployment and change performance, setting a higher bar for cross-organization standards. Collaboration and incident response become more efficient through shared standards.
Requirements
A strong foundation in computer science with hands-on experience in DevOps, CloudOps, or Platform engineering practices ensures resilient, scalable infrastructure for complex workloads.
Experience developing and managing scalable cloud infrastructure, ideally in science, machine learning, or data processing-heavy contexts, helps complex workflows run smoothly for demanding use cases.
End-to-end or full-stack system design experience, ideally supporting physics, engineering, or AI teams, keeps multi-team efforts coordinated with clear structure.
Proficiency in Python and Linux underpins automation, scripting, and interaction with compute environments, forming the basis for most infrastructure tooling used daily.
Problem solvers are driven to solve complex problems with simple solutions and contribute to clean energy, aligning the role with the company mission of enabling abundant, safe, clean energy through stellarator design.
Practical notes
This role is based in Munich and operates as a full-time position. The team values diversity of thought and experience. Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.