Preferred Candidate Profile

Qualifications & Experience

• 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.