Machine Learning Researcher

<p><span style="color: #3d74cc; font-size: x-large;"><strong>About Us</strong></span></p> <p data-renderer-start-pos="74"><span style="color: rgb(61, 116, 204);"><strong>AXQ Capital</strong></span> is a global quantitative investment firm with offices in <span style="color: rgb(61, 116, 204);"><strong>New York, Beijing, Shanghai, and Hong Kong</strong></span>. 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.</p> <p data-renderer-start-pos="74"> </p> <p><span style="font-size: 18pt; color: rgb(61, 116, 204);"><strong>Job Duties </strong></span></p> <p data-renderer-start-pos="451">As a<span style="color: rgb(61, 116, 204);"><span class="Apple-converted-space"> </span><strong>Machine Learning Researcher</strong></span>, 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.</p> <p data-renderer-start-pos="451"> </p> <p data-renderer-start-pos="818"><span style="font-size: 18pt; color: rgb(61, 116, 204);"><strong>Qualifications</strong></span></p> <p class="p1">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.</p> <ul> <li>Master’s or PhD from a top-tier university in a quantitative discipline (e.g., mathematics, physics, statistics, computer science, or related fields)</li> <li>Strong foundation in machine learning, statistics, and optimization, with deep expertise in at least one area such as time series modeling, NLP, or LLMs</li> <li>Strong communication skills and ability to collaborate effectively in a research-driven environment</li> <li>Experience with the Python scientific and ML ecosystem (e.g., NumPy, Pandas/Polars, PyTorch, TensorFlow)</li> <li>Proven ability to build and improve models that perform under noisy, non-stationary, and high-dimensional data conditions</li> <li>Intellectually curious, highly analytical, and driven to discover alpha in complex systems</li> <li>Self-motivated and highly productive, with strong ownership and a bia