Machine Learning Researcher

About the role

AXQ Capital is a global quantitative investment firm with offices in New York, Beijing, Shanghai, and Hong Kong . We pursue consistent alpha through rigorous scientific research and sustained investment in technology and data infrastructure. Our strategies are deployed across global markets, spanning multiple geographies, asset classes, and trading horizons. Job Duties As a Machine Learning Researcher , you will develop ML/DL models to generate alpha from large-scale financial and alternative datasets. Working closely with portfolio managers and senior quantitative researchers, you will conduct research across a range of trading strategies within a fast, iterative research-feedback loop.

Requirements

• Below is a list of skills and experiences we believe are relevant. Even if you don't consider yourself a perfect match, we still encourage you to apply because we are committed to helping our people grow and develop.
• Master's or PhD from a top-tier university in a quantitative discipline (e.g., mathematics, physics, statistics, computer science, or related fields)
• Strong foundation in machine learning, statistics, and optimization, with deep expertise in at least one area such as time series modeling, NLP, or LLMs
• Strong communication skills and ability to collaborate effectively in a research-driven environment
• Experience with the Python scientific and ML ecosystem (e.g., NumPy, Pandas/Polars, PyTorch, TensorFlow)
• Proven ability to build and improve models that perform under noisy, non-stationary, and high-dimensional data conditions
• Intellectually curious, highly analytical, and driven to discover alpha in complex systems
• Self-motivated and highly productive, with strong ownership and a bias toward action
• Publications at top-tier venues such as NeurIPS, ICML, KDD, or IJCAI
• Strong track record in competitive ML platforms (e.g., Kaggle) or academic competitions
• Prior experience in quantitative research, systematic trading, or alpha signal generation

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