Machine Learning Engineer
Summary
Builds AI agents and models for high-frequency trading, using deep learning and time-series forecasting to monitor systems and support research workflows.
About Cubist
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.
Role/Responsibilities:
We are seeking a Machine Learning Engineer to join the High Frequency Trading Technology team.
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.
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.
Requirements:
- PhD or PhD candidate in machine learning, computer science or other AI related research fields
- Experience with sequential modeling and time series forecasting using deep learning
- Experience with deep neural networks and representation learning
- Prior experience working in a data driven research environment
- Experience with translating mathematical models and algorithms into code
- Proficiency in programming languages such as Python and R
- Experience with machine learning software libraries such as TensorFlow or PyTorch
- Experience implementing Agent or Context engineering is strongly preferred
- Experience with natural language processing technology is strongly preferred
- Excellent analytical skills, with strong attention to detail
- Collaborative mindset with strong independent research ability
- Strong written and verbal communication skills
- Commitment to the highest ethical standards
Skills
As published by greenhouse · 6 questions · 1 written answer
Basics
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