Deep Learning Quantitative Researcher

About the role

We are seeking a Deep Learning Quantitative Researcher to design and build core deep‑learning pipelines that support applied quantitative alpha research. The role involves end‑to‑end model development, from data preparation through production deployment, within a fast‑paced quantitative trading environment.

Key responsibilities

• Design and implement deep learning pipelines for quantitative alpha research, covering data preparation, distributed training, evaluation, and production deployment.
• Drive the research agenda using applied deep learning, handling problem formulation, model design, training, validation, and performance attribution.
• Ensure rigorous research discipline with strict out‑of‑sample hygiene, leakage prevention, and benchmarking against simpler baselines.
• Serve as the central deep learning expert, advising on architecture selection, training diagnostics, reviewing model designs, and setting evaluation standards.
• Facilitate seamless model fitting and computation across teams via standardized training and inference interfaces and reusable components.

Required profile

• PhD or top‑tier academic background in Computer Science, Engineering, Physics, Mathematics, or Statistics from a globally top‑20 university.
• 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 or a leading AI/technology company.
• Proven end‑to‑end ownership of the deep learning model lifecycle on a significant production system or published research line.

Required skills

• Python programming
• Deep learning frameworks (e.g., TensorFlow, PyTorch)
• Distributed/multi‑GPU training and mixed‑precision techniques
• Performance profiling and optimization of large‑scale models
• Strong foundations in statistics, optimization, and machine learning theory
• Experience with large‑scale datasets and efficient data loading (columnar formats, streaming)