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 firms 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 firms 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
35 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.