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.
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