About the position

We are seeking an ambitious and driven intern to join our dynamic team in Berlin. In this role, you will collaborate with quantitative researchers, machine learning experts, and software engineers to advance our cutting-edge quantitative investment platform.

Your Mission

• Build and Maintain Research Infrastructure: Design and implement scalable software systems to support large-scale financial research and backtesting.
• Optimize Code for Performance: Improve efficiency and scalability by optimizing and parallelizing code, enabling high-performance execution of large-scale modeling, simulations, and backtesting.
• Ensure Code Quality: Write clean, modular, and maintainable code. Follow best practices in software development, including version control, unit testing, and peer reviews.
• Simplify and Streamline the Codebase: Refactor existing code to reduce complexity, improve readability, and ensure consistency for easier maintenance and scalability.
• Enhance Model Explainability: Develop tools to interpret and visualize model outputs, making complex insights accessible to both technical and non-technical stakeholders.
• Collaborate with Cross-Functional Teams: Work closely with quantitative researchers, machine learning practitioners, and software engineers to understand and address both technical and business requirements.
• Communicate Technical Concepts: Clearly and concisely explain complex ideas to non-technical stakeholders.

Your Skillset

To thrive in this role, you should possess:

• A degree in a technical field such as Software Engineering, Mathematics, Physics, Computer Science, Data Science, or Statistics.
• Strong programming skills in Python, with hands-on experience using libraries for machine learning, data analysis, and financial modeling (e.g., scikit-learn, SciPy, NumPy, pandas, and joblib). Experience with Polars is a plus.
• Experience in collaborative Python development, including proficiency with tools for version control (Git), shell scripting (Zsh/Bash), and continuous integration (e.g., GitHub).
• The ability to write maintainable and well-tested code using tools like Pydantic and Pytest.
• A solid understanding of machine learning and applied statistics.
• A keen interest in financial markets and the quantitative investment process.

Why us?

• An exciting and hands-on role with real impact on shaping the future of AI-driven sustainable investing.
• A collaborative company culture focused on quality, openness, and attention to detail in all areas of our work.
• The opportunity to work in an interdisciplinary team alongside experienced professionals in quantitative finance, software engineering, and machine learning.
• A modern loft office in Berlin (Prenzlauer Berg), healthy food options (coffee, juices, fruit, vegetables, sandwiches), and high-end Apple hardware.
• Internal workshops, unique learning opportunities across a wide range of domains, and fantastic team events.
• Additional benefits such as Urban Sports Club or EGYM Wellpass, as well as access to corporate benefits.

About usWe are a deep tech pioneer, providing AI-based investment solutions to our clients. Building on the best practices of quantitative asset management we tap the potential of machine learning for sustainable investments on capital market.
Our interdisciplinary team consists of experts in the fields of finance, computer science, software engineering, machine learning as well as mathematics, physics, and neuroscience. Ultramarin is embedded in the global AI community, through a close exchange with leading universities and as a member of Inquire Europe. 
To date Ultramarin is one of the leading drivers for AI-based analyses and decision-making processes in asset management. Ultramarin has been founded in 2017, is headquartered in Berlin, with additional locations in Frankfurt and Munich, and is backed by leading international business angels and VCs.