Deep Learning Quantitative Researcher

About the role

We are seeking a Deep Learning Quantitative Researcher to lead the design and implementation of core deep learning pipelines for applied alpha research. The role combines advanced research with production‑grade engineering in a fast‑moving quantitative finance environment.

Key responsibilities

• Design and build end‑to‑end deep learning pipelines, from data preparation and distributed training to evaluation and production deployment.
• Drive the research agenda using applied deep learning, owning the full empirical loop: problem formulation, model design, training, validation, and performance attribution.
• Maintain rigorous out‑of‑sample hygiene, prevent data leakage, and benchmark against simpler baselines.
• Serve as the firm’s central deep learning expert, advising on architecture selection, training diagnostics, and model evaluation standards.
• Standardize training and inference interfaces to enable seamless model fitting across teams and systems.

Required profile

• Top‑tier academic background (top‑20 global university) with PhD‑level training in Computer Science, Engineering, Physics, Mathematics, or Statistics.
• Gold medal in a national or international olympiad (e.g., IMO, IOI, IPhO) strongly preferred.
• 3–5 years of professional experience applying deep learning to large‑scale problems, ideally in quantitative finance.
• Proven end‑to‑end ownership of a deep learning model lifecycle on a production system or published research line.

Required skills

• Python programming.
• Modern deep‑learning frameworks (e.g., PyTorch, TensorFlow).
• Distributed/multi‑GPU training, mixed‑precision, and throughput optimization.
• Working knowledge of C++ or CUDA‑level optimization.
• Experience with large‑scale dataset handling, columnar formats, and streaming data loaders.