Graduate Deep Learning Researcher - New York

Job description

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 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** - Build and improve deep learning models to enhance trading performance across multiple asset classes, working under the guidance of senior researchers - Contribute to research in areas such as representation learning, transformers, foundation models, generative AI, reinforcement learning, and other emerging deep learning techniques - Support large-scale data curation, feature representation, and self-supervised and multimodal learning from structured and unstructured datasets - Contribute to shared training infrastructure and deployment pipelines - Stay current with academic and industry developments in deep learning and share relevant research with the team **Your Skills and Experience** - PhD, Master's or Bachelor’s degree (completed to start full time in February or August 2027, or currently in a postdoctoral position) in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, Engineering, or a related quantitative field - 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 - Hands-on experience with modern deep learning architectures, such as transformers, sequence models, representation learning, self-supervised learning, and/or generative models - Solid understanding of the theoretical foundations of deep learning, optimization, statistical learning, and neural network training dynamics - Strong programming skills in Python and hands-on experience with at least one modern deep learning framework, such as PyTorch, JAX, or TensorFlow - Strong communication skills with the ability to explain technical ideas clearly to both technical and non-technical audiences 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. The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit [Benefits - US | IMC Trading](https://www.imc.com/us/careers/benefits) for more comprehensive information. Base Salary: $300,000 **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.*