Deep Learning Quantitative Researcher

Please submit resumes to [email protected] and reference REQ-30088.

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.