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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, and upholding rigorous research discipline in a low signal-to-noise domain.

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.
• Working knowledge of C++ or CUDA-level optimization a plus.
• Familiarity with LLM tooling as a research accelerant a plus.
• 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
• Large-scale Datasets
• Columnar Formats
• Streaming Data Loaders
• Experiment Management Tooling
• Experiment Tracking
• Hyperparameter Optimization
• Reproducible Research Environments
• C++
• CUDA
• LLM Tooling

Experience Level

• 3-5 years

Education Level

• PhD