Software Engineer – Trading Risk Modelling (Python)

ING Hubs Poland is hiring!

The expected salary for this position: 9600- 22 000PLN

The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.

Are you an experienced software engineer who enjoys building high‑quality libraries, designing robust architectures, and raising engineering standards? Do you want to apply your craft in a complex, data‑driven environment within financial markets?
If so, this role offers you the opportunity to shape the core engineering foundations of our Trading Risk modelling.

We are looking for you if you:

• Have 3–5 years of software development experience in Python.
• Take on responsibility. In return, we’ll back you to develop into an even more
• Excel in writing modular, testable, and maintainable code.
• Have practical experience with CI/CD, DevOps tooling, code reviews, and automated testing.
• Enjoy building libraries, frameworks, or shared tooling for other developers.
• Communicate clearly and enjoy working with multidisciplinary teams (quants, risk managers, traders).
• Bring a proactive attitude, ownership mindset, and collaborative spirit.
• Care deeply about software quality and delivering sustainable, well‑designed systems.
• Stay curious and keep learning.

You'll get extra points for:

• Familiarity with quantitative models, derivatives pricing, or (trading market) risk frameworks (EC, VaR, FRTB, Stress Testing).
• Exposure to financial products or financial markets.

Your responsibilities:

As a Software Engineer in the Global Models Team, you will be responsible for creating the development frameworks, tools, and libraries that power our risk‑model ecosystem. Your work will accelerate the delivery of quantitative models and ensure they run reliably.

You will:

• Design and build internal Python libraries used by multiple modelling teams for risk, valuation, and stress testing.
• Set and enforce coding standards, best practices, and development workflows (Git, branching strategies, code reviews, CI/CD pipelines, packaging).
• Implement scalable, production‑grade components for model integration, testing, and monitoring.
• Introduce modern engineering practices such as automated testing, type checking, linting, and deployment automation.
• Collaborate with quants, translating mathematical designs into clean software architectures and guiding others on good engineering principles.
• Translating mathematical model specifications into robust, well‑tested software components.
• Building model‑monitoring tools and supporting integration into existing frameworks.
• Own the long‑term maintainability of the model‑development platform, including documentation, versioning, and library governance.

The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.