Deep Learning Quantitative Researcher
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Reach the decision-maker — $5About the role
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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