Junior Deep Learning Researcher job opportunity at Pinely.

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Date2026-04-05T16:34:48.616Z bot
Pinely Junior Deep Learning Researcher
Experience: General
Pattern: Full-time
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loacation Amsterdam, Netherlands
loacation Amsterdam....Netherlands
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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. Our flat organizational structure encourages autonomy, creativity, and direct ownership. We value an informal, idea-driven culture where innovation is celebrated and every contribution matters. We are excited to announce an opportunity for a Junior Deep Learning Researcher to join our Amsterdam-based office. Responsibilities 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).

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