Overview
Build the software backbone letting AI 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 Pyt
Full job description
Your Mission
Build the software backbone letting AI 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 orchestrate real 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
Qualifications And Requirements
- 2–5 years of software engineering experience, with solid Python fundamentals
- Comfort working across the stack. You don't need to be an expert in everything, but you're willing 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, additional languages preferred
Requirements & qualifications
- 2–5 years of software engineering experience, with solid Python fundamentals
- Comfort working across the stack. You don't need to be an expert in everything, but you're willing 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, additional languages preferred
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