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.