Deep Learning Quantitative Researcher

Deep Learning Quantitative Researcher Preferred Candidate Profile * Top-tier academic background from a globally top-20 university (e.g., MIT, Harvard, Princeton, Stanford, Caltech) * PhD-level training in Computer Science, Engineering, Physics, Mathematics, or Statistics preferred * Gold medal in a national or international olympiad (IMO, CMO, IOI, NOI, IPhO, CPhO) strongly preferred * Practical, hands-on experience with large-scale, end-to-end deep learning at a top-tier quantitative trading firm or a leading AI/technology company preferred Key Responsibilities * Design and build the firm’s core deep learning pipelines for applied quantitative alpha research— from data preparation and distributed training through evaluation and production deployment. * Drive a significant part of the research agenda using applied deep learning techniques, owning the full empirical loop: problem formulation, model design, training, validation, and performance attribution. * Uphold rigorous research discipline in a low signal-to-noise domain — strict out-of-sample hygiene, leakage prevention, and honest benchmarking against simpler baselines. * Act as the firm’s central point of deep learning expertise: advise on architecture selection and training diagnostics, review model designs, and set standards for how models are evaluated and promoted. * Facilitate the seamless flow of model fitting and model computation across teams and systems through standardized training and inference interfaces and reusable components. Qualifications \& Experience * 3–5 years of professional experience applying deep learning to large-scale problems, ideally in quantitative finance. A strong PhD research record plus hands-on experience training large models at a leading AI/technology company will be considered in lieu of direct quant experience. * Proven end-to-end ownership of the deep learning model lifecycle on at least one significant production system or published research line. * Deep expertise in Python and a modern DL framework. * Hands-on experience with large-scale model training: distributed/multi-GPU training, mixed precision, and throughput profiling and optimization. * Strong foundations in statistics, optimization, and machine learning theory. Hard Skills \& Technical Knowledge: * Command of modern deep learning architectures, and the judgment to know when a simpler model should win. * Practical technique for low signal-to-noise learning: regularization, ensembling, and validation protocols that survive out-of-sample. * Experience with large-scale datasets — efficient columnar formats, streaming data loaders, and point-in-time-correct dataset construction. * Fluency with experiment-management tooling: experiment tracking, hyperparameter optimization, and reproducible research environments. * Working knowledge of C++ or CUDA-level optimization a plus; familiarity with LLM tooling as a research accelerant a plus. Soft Skills: * Research Taste \& Rigor: Designs clean experiments and kills ideas quickly when the evidence says so. * Proactive Collaboration: Builds strong partnerships across research and engineering. * High Integrity: Upholds rigorous ethical standards in handling sensitive data and models. * Growth Mindset: Stays current with a fast-moving field and adopts what works. * Superb Communication: Explains model behavior and uncertainty to technical and nontechnical audiences.