Join Ortec Finance as a Junior Research Engineer! Apply ML & optimization to real-world finance, build scalable solutions, and innovate in a collaborative R&D environment. Apply now!

WHO ARE YOU?

You enjoy collaboration and are ready to take on challenges. We’re looking for an individual who not only excels in teamwork but also embraces risks and likes exploring new things. And of course, you identify yourself with our core values.

Requirements:

• You have a bachelor’s or master’s degree in Computer Science, or comparable experience and background
• A foundational understanding of machine learning, with a strong motivation to deepen expertise and grow in this area
• Strong programming skills in Python, ideally together with experience in machine learning libraries like PyTorch or Tensorflow
• Open to learn and contribute to Java and C# code bases
• You are fluent in English

You are a recent graduate with a technical background and a strong interest in applying cutting-edge machine learning and optimization techniques to tackle complex financial challenges. You are motivated to translate these techniques into scalable technical solutions that others can widely adopt. You’ll be working in a dynamic, collaborative environment where innovation, experimentation, and impact go hand in hand.
As a Junior Research Engineer, you’ll be joining the R&D Labs team, where we conduct research that can be applied across various departments to help improve the performance of their products. While part of your work will be undertaken in the lab, you’ll spend roughly half of your time collaborating directly with engineering teams outside the lab. You’ll collaborate with researchers and engineers from other departments to disseminate lab results to their eventual applications in our software products.

You will:

• Develop algorithms/models in a scalable way and treat them as software artifacts. By integrating model development in release workflows (CI/CD).
• Produce efficient and sustainable code and software architectures that allow for smooth integration of newly developed methodologies.
• Work together with researchers, quants, and software engineers on state-of-the-art research and applications in machine learning and econometrics. Projects include Bayesian modeling, optimization, and generative machine learning.
• Conduct experiments using MLOps platforms such as Azure Machine Learning, as well as other comparable suites, to evaluate and enhance model development and deployment workflows.
• Support and Enable Innovation projects with the technical implementations of research results.
• Manage cloud-based data and compute platforms to ensure scalable and efficient infrastructure for data-driven applications.