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About the Role

This role focuses on designing and building core deep learning pipelines for applied quantitative alpha research, driving the research agenda with deep learning techniques, and serving as the firm's central point of deep learning expertise.

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.

Requirements

• 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.
• 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.
• Designs clean experiments and kills ideas quickly when the evidence says so.
• Builds strong partnerships across research and engineering.
• Upholds rigorous ethical standards in handling sensitive data and models.
• Stays current with a fast-moving field and adopts what works.
• Explains model behavior and uncertainty to technical and nontechnical audiences.

Skills

• Deep Learning
• Python
• Distributed Training
• Multi-GPU Training
• Mixed Precision
• Throughput Profiling
• Optimization
• Machine Learning Theory
• Statistics
• Regularization
• Ensembling
• Validation Protocols
• Large-scale Datasets
• Columnar Formats
• Streaming Data Loaders
• Experiment Tracking
• Hyperparameter Optimization
• Reproducible Research Environments
• C++
• CUDA
• LLM Tooling

Experience Level

• 3-5 years

Education Level

• PhD-level training in Computer Science, Engineering, Physics, Mathematics, or Statistics preferred
• Top-tier academic background from a globally top-20 university