Junior Deep Learning Researcher

• Conduct original research in the areas of artificial intelligence, machine learning, and related quantitative fields.
• Develop and experiment with modern deep learning architectures
• Analyze large, unstructured, and noisy datasets to extract meaningful insights
• Collaborating with developers and other researchers to implement and optimize trading strategies.
• Continually explore new methodologies and technologies to enhance research outcomes

• A degree in mathematics, physics, computer science, or another quantitative discipline (or expectation of such a degree within the next year)
• Knowledge of machine learning, probability theory and mathematical statistics.
• Proficiency in Python.
• Some experience with C++, although not necessarily in an industrial setting.
• Practical experience with modern DL architecture.
• Background in analyzing large, unstructured, and noisy datasets.
• Would be beneficial: Published research findings in top-tier journals and conferences (ICML, NeurIPS, ICLR, CVPR, ICCV).

• High base salary plus significant biannual bonuses;
• Relocation package to Amsterdam (we are very flexible in discussing salary and conditions of employment);
• Flexible workflow and working schedule;
• Being part of a team with top winners in mathematics and programming competitions;
• Cutting-edge hardware and software in production as well as high technical expertise of the company which allows implementation of ideas and boosting great results;
• Internal training, comprehensive health insurance, sports reimbursement, and biannual corporate events.

We are Pinely, a high-frequency algorithmic trading firm based in Amsterdam.

Our team specializes in developing robust and adaptive strategies applicable across a wide range of financial instruments and exchanges. We actively support the Olympiad movement, and many of our colleagues are prize-winning mathematicians, researchers and engineers.

Researchers at Pinely benefit from working in a fast-paced HFT environment, where the impact of their ideas is quickly visible in production. The research teams are supported by a world-class infrastructure group that ensures smooth deployment and reliable experimentation at scale.