Software Engineer, Infrastructure

Pegel; Berlin; Hybrid; EUR 65,000 - 100,000; Full time

Job summary

Dunia builds software that coordinates robots, experiments, simulations, and learning systems. As Software Engineer, Infrastructure, you own the systems that connect software decisions to real-world execution, accelerating discovery through reliable, scalable infrastructure.

Responsibilities include building backend services in Python, data pipelines, dashboards, CI/CD, and observability, with collaboration across science and engineering teams.

Qualifications

• 2–5 years of software engineering experience with solid Python fundamentals.
• Comfort across the stack; willingness to explore unfamiliar areas.
• Experience with containerized applications and basic DevOps practices.
• Clear, maintainable code and good communication.

Responsibilities

• Build backend services and data pipelines for experiments and workflows.
• Develop dashboards/interfaces used by scientists and engineers.
• Maintain infrastructure with CI/CD and observability.
• Collaborate across software, compute, and science teams.

Skills

Python fundamentals

Containerized apps

DevOps practices

Communication

English fluency

Tools

FastAPI

Pydantic

React/TS

SLURM

Job description

Your mission

Build the software backbone lettingAI reason about and experiment on the physical world

Dunia is building AI for one of the hardest unsolved problems in science: turning materials discovery from an academic, trial-and-error process into a programmable, scalable discipline.

At Dunia software doesn’t just serve users, it coordinates robots, experiments, simulations, and learning systems that interact with matter itself.

As Software Engineer, Infrastructure, you will own the systems that make this possible. Your work sits directly between scientific intent and physical execution: if infrastructure is unclear, brittle, or slow, discovery stalls. If it’s well-designed, the entire system accelerates.

This role is for an engineer who wants their software decisions to have real, measurable consequences in the world.

Your tasks will include:

Production systems that matter

• Backend services in Python (FastAPI,Pydantic) that orchestratereal scientific workflows
• Data pipelines that turn raw experimental output into usable signal

Tools people rely on daily

• Internal dashboards and interfaces (React, TypeScript, Tailwind,Streamlit) used by scientists and engineers
• Systems where usability and correctness directly affect productivity

Infrastructure as a first-class product

• Containerized environments and CI/CD pipelines that keep the system moving
• Observability that tells youwhat’shappening before users complain
• Reliability improvements driven by actual failure modes, not hypotheticals

The full loop: software → compute → science

• Job orchestration and ML system support
• Integration with SLURM-based simulation clusters
• Infrastructure decisions informed by real workloads, not abstract benchmarks

Your profile

• 2–5 years of software engineering experience, with solid Python fundamentals
• Comfort working across the stack. Youdon'tneed to be an expert in everything, butyou'rewilling to dig into unfamiliar territory
• Experience with containerized applications and basic DevOps practices
• Clear communication and a habit of writing clean, maintainable code
• Bonus: exposure to scientific computing, ML infrastructure, or data-intensive systems
• English fluency,additionallanguages preferred