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About the role
<p><strong><u>About Cubist</u></strong></p>
<p>Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.</p>
<p><strong><u>Role/Responsibilities:</u></strong></p>
<p>We are seeking a Machine Learning Engineer to join the High Frequency Trading Technology team.</p>
<p>This role will apply the latest AI technologies to solve various real-world problems and streamline day-to-day operations, such as creating a production support AI agent that helps monitor production problems and suggest actions.</p>
<p>This role will also work with the AI research group on various projects such as creating synthetic data for training and using MCP agents to streamline research workflow.</p>
<p><strong><u>Requirements: </u></strong></p>
<ul>
<li>PhD or PhD candidate in machine learning, computer science or other AI related research fields</li>
<li>Experience with sequential modeling and time series forecasting using deep learning</li>
<li>Experience with deep neural networks and representation learning</li>
<li>Prior experience working in a data driven research environment</li>
<li>Experience with translating mathematical models and algorithms into code</li>
<li>Proficiency in programming languages such as Python and R</li>
<li>Experience with machine learning software libraries such as TensorFlow or PyTorch</li>
<li>Experience implementing Agent or Context engineering is strongly preferred</li>
<li>Experience with natural language processing technology is strongly preferred</li>
<li>Excellent analytical skills, with strong attention to detail</li>
<li>Collaborative mindset with strong independent research ability</li>
<li>Strong written and verbal communication skills</li>
<li>Commitment to the highest ethical standards</li>
</ul>
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