Point72
Research Engineer, Knowledge Graph Intelligence
About this role
The Research Engineer in Knowledge Graph Intelligence at Point72 will focus on developing machine learning solutions, particularly in natural language processing to support investment professionals. This role involves the full research process, including data analysis and model testing, while collaborating across teams to implement these solutions in production systems.
What you'll do
- Develop algorithmic models for machine learning applications
- Specialize in NLP and data analysis from unstructured sources
- Conduct data evaluation and feature engineering
- Implement GenAI solutions and optimize model performance
- Collaborate with cross-functional teams for system integration
- Stay current with advancements in machine learning and NLP
What they're looking for
- PhD or master's degree in a related field or equivalent experience
- 6+ years of experience in machine learning and algorithm development
- Proficiency in Python and relevant ML libraries
- Experience with LLMs and deep learning systems
- Familiarity with sparse data techniques
- Strong understanding of NLP concepts and architectures
- Experience in data evaluation and model explainability
- Linux environment experience
Benefits
- Fully-paid health care benefits
- Generous parental and family leave policies
- Tuition assistance
- Mental and physical wellness programs
- 401(k) savings program with employer match
- Volunteer opportunities
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Point72
Point72 operates trading, data infrastructure, and investment management technology platforms that power systematic portfolio management and capital markets operations. The company is hiring infrastructure engineers, data reliability specialists, and software engineers to build and maintain mission-critical systems including data pipelines, network architectures, storage platforms, and research tools.
- Website
- point72.com
Likely interview questions
- Walk us through a recent NLP project where you built and deployed a production model. What were the key challenges with unstructured text data, and how did you handle them?
- Describe your experience fine-tuning large language models or foundation models. What techniques have you used for optimization, and how did you evaluate performance?