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Graduate Deep Learning Researcher - London

London, United Kingdom

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At a glance

Education: PhD

Skills mentioned

deep learningmachine learningstatisticspythonpytorchtensorflow

About the role

IMC is seeking Deep Learning Researchers to join our growing team. As a Deep Learning Researcher, you will develop and apply state-of-the-art deep learning techniques at the intersection of AI and markets. You will help accelerate our efforts in areas such as modern neural network architectures and representation learning, improving our large-scale AI models that increasingly shape our trading. You will work closely with senior researchers to gain exposure to the full research lifecycle, from initial idea to production deployment. The ideal candidate has a strong foundation in modern AI and machine learning research, gained through post-graduate study or internships. Prior financial industry experience is not required. We are looking for someone who brings rigorous scientific thinking and a drive to learn, and who wants to grow alongside experienced researchers as we build a world-class deep learning capability at IMC. Your Core Responsibilities <ul> <li>Build and improve deep learning models to enhance trading performance across multiple asset classes, working under the guidance of senior researchers</li> <li class="li1">Contribute to research in areas such as representation learning, transformers, foundation models, generative AI, reinforcement learning, and other emerging deep learning techniques</li> <li class="li1">Support large-scale data curation, feature representation, and self-supervised and multimodal learning from structured and unstructured datasets</li> <li class="li1">Contribute to shared training infrastructure and deployment pipelines</li> <li class="li1">Stay current with academic and industry developments in deep learning and share relevant research with the team</li> </ul>   Your Skills and Experience <ul class="ul1"> <li>A minimum of an MSc (PhD preferred) in Machine Learning, Statistics, Deep Learning, Probabilistic Programming, or a related quantitative field</li> </ul> <ul class="ak-ul" data-local-id="d86946f6-cee0-4951-8ebf-d6466f184f7e" data-prosemirror-content-type="node" data-prosemirror-node-name="bulletList" data-prosemirror-node-block="true"> <li data-local-id="78bd751b-c85c-4a73-b7f5-9f65ae132ad2" data-prosemirror-content-type="node" data-prosemirror-node-name="listItem" data-prosemirror-node-block="true"> Currently in your final year of study, graduating in 2027 </li> <li data-local-id="e2a7bff7-8a28-4a88-95de-8bf964eed05d" data-prosemirror-content-type="node" data-prosemirror-node-name="listItem" data-prosemirror-node-block="true"> A minimum of an MSc (PhD preferred) in Machine Learning, Statistics, Deep Learning, Probabilistic Programming, or a related quantitative field </li> <li data-local-id="e2a7bff7-8a28-4a88-95de-8bf964eed05d" data-prosemirror-content-type="node" data-prosemirror-node-name="listItem" data-prosemirror-node-block="true"> Demonstrated experience in deep learning research or applied machine learning. For PhD candidates, we expect a record of research contributions in your area published at leading ML venues (e.g., NeurIPS, ICML, ICLR). For Bachelor’s and Master’s candidates, research experience is required but can take other forms, e.g. thesis work, open-source contributions, or projects </li> <li class="li1">Hands-on experience with modern deep learning architectures, such as transformers, sequence models, representation learning, self-supervised learning, and/or generative models</li> <li class="li1">Solid understanding of the theoretical foundations of deep learning, optimization, statistical learning, and neural network training dynamics </li> <li class="li1">Strong programming skills in Python and hands-on experience with at least one modern deep learning framework, such as PyTorch, JAX, or TensorFlow </li> <li class="li1">Strong communication skills with the ability to explain technical ideas clearly to both technical and non-technical audiences</li> </ul> You may submit one application per role each year.  We strongly encourage you to focus on applying to a single role that best matches your skills and interests.  Though you may apply to multiple roles, please note that each application will be evaluated based on the specific criteria established for that particular role. If you have already applied for this position during the current recruitment season and were not selected, you may reapply when the next recruitment season begins in 2027. Please note that, due to current regulations with the Immigratie en Naturalisatiedienst (IND), IMC is unable to obtain immigration sponsorship for candidates who currently have citizenship from Russia, Belarus or Iran, and attend university in one of the aforementioned countries. We greatly appreciate your understanding and hope to consider you for future opportunities. If you have citizenship from Ukraine, you will need to confirm that you currently hold a biometric passport so that IMC can obtain the correct immigration sponsorship for you. Please confirm by emailing <a ----- data-prosemirror-content-type="mark" ----- If you are currently residing or attending university outside of Russia, Belarus, or Iran, and have existing work authorisation in the Netherlands or are eligible to work without sponsorship, we encourage you to apply and inform our campus recruitment team of your situation: <a ----- data-prosemirror-content-type="mark" ----- class="content-conclusion">About Us IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.  </div>

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