Deep Learning Quantitative Researcher

Deep Learning Quantitative Researcher Please submit resumes to QuantTalentEUR@mlp.com  and reference REQ-30088. 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.