About the role

Aplyr's Quick Take

This role is for a Machine Learning Engineer focused on applying AI to enhance trading operations and support research at a high-frequency trading firm. You'll be developing AI solutions for monitoring and improving production systems and collaborating on research projects involving synthetic data and workflow optimization.

Good fit

Ideal candidates will have a PhD or be a PhD candidate in machine learning or a related field, with experience in deep learning and a strong analytical mindset. A collaborative approach and the ability to work independently on research are key.

Worth noting

The position emphasizes a commitment to ethical standards, which is crucial in the finance sector. The focus on both production support and research collaboration suggests a dynamic work environment, but the role may involve high-pressure situations typical of trading firms.

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 & Tags

python go machine learning ai data product

Aplyr's read

Point72 is a global asset management firm known for its multi-strategy investing and rigorous analyst programs, attracting driven professionals in finance and technology.
Synthesized from recent postings & public sources

What's promising

•Point72's multi-strategy approach offers diverse investment exposure and learning opportunities.
•The firm invests heavily in technology, providing cutting-edge tools for employees.
•Point72 Academy programs are renowned for developing top-tier investment professionals.

What to watch

•The firm's high-performance culture may lead to intense work pressure.
•Regulatory scrutiny is a constant concern due to past controversies.
•Limited public information about work-life balance and employee satisfaction.

Why Point72

•Point72 manages the personal capital of founder Steven A. Cohen.
•The firm combines traditional and quantitative strategies in its investment approach.
•Point72 offers specialized roles in macro technology and quantitative analysis.

Aplyr’s read is generated by AI from public sources. Was it useful?

About Point72

Point72

point72.com

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Point72 Asset Management is a global asset management firm that manages the capital of its founder, Steven A. Cohen, and certain employees. The firm employs a multi-strategy approach to investing, focusing on long/short equity, macroeconomic trends, and quantitative strategies.