Research Engineer, Knowledge Graph Intelligence

Skills this role screens for

About the role

A Career with point72’s Surveillance team

Point72’s Surveillance team sets the industry standard for intelligence-driven surveillance by proactively identifying, monitoring, and assessing various sources of compliance risk using proprietary tools and specialized tradecraft. We support senior management by providing strategic assessments, actionable recommendations, and real-time escalations. At Point72, members of the Surveillance team conduct integrated trade and communication surveillance and collaborate to turn information into intelligence for our internal customers. The team also monitors employee activity for evidence of violations of applicable federal securities laws, internal compliance policies and procedures, and relevant rules and regulations enforced by the SEC, FINRA, and other organizations.

The work

What you’ll do

As a Machine Learning Engineer - Applied Scientist you will play a critical role in developing algorithmic solutions and models for production-ready applications that support our front office investment professionals. You will specialize in natural language processing (NLP) solutions that extract insights from unstructured text data, with additional capabilities in predictive modeling, clustering, and time series analysis. You will manage all aspects of the research process including methodology selection, data collection and analysis, implementation and testing, prototyping, and performance evaluation. You will apply, adapt, and extend existing results in the broad field of NLP, while also conducting novel research as required. Specifically, you will:

• Contribute to projects across various machine learning (ML) disciplines, including NLP, unstructured data analysis, predictive modeling, and classic machine learning.
• Implement GenAI solutions, utilize ML infrastructure, and contribute to modeling, data preparation, optimization, and performance enhancements.
• Work with sparse data and apply techniques to improve model accuracy and generalization.
• Conduct data evaluation, including data preprocessing, feature engineering, and model performance assessment.
• Collaborate cross-functionally with data engineers, software developers, and product teams to integrate models into production systems.
• Stay up to date with the latest advancements in natural language processing and machine learning, applying new techniques as needed.

Also

What’s REQUIRED

We take care of our people

We invest in our people, their careers, their health, and their well-being. When you work here, we provide:

About Point72

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry's brightest talent by cultivating an investor-led culture and committing to our people's long-term growth. For more information, visit https://point72.com/.

The annual base salary range for this role is $175,000-$250,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.

• PhD, master's degree, or 4+ years of CS, CE, ML or related field experience.
• 6+ years of experience building ML models and developing algorithms.
• Strong proficiency in Python, and hands-on experience with NumPy, Hugging Face, PyTorch, and spaCy for NLP applications.
• Prior experience in the domains of LLMs, foundation models, or large-scale deep learning systems, with a complete understanding of modern training, fine-tuning, quantization, and model evaluation.
• Expertise in working with sparse data and applying techniques such as data augmentation, weak supervision, and semi-supervised learning.
• Solid grasp of NLP concepts, including tokenization, embeddings, attention mechanisms, and transformer-based architectures.
• Experience with data evaluation techniques, model explainability, and error analysis.
• Experience working in a Linux environment.
• Commitment to the highest ethical standards.
• Fully-paid health care benefits
• Generous parental and family leave policies
• Volunteer opportunities
• Support for employee-led affinity groups representing women, people of color, and the LGBT+ community
• Mental and physical wellness programs
• Tuition assistance
• A 401(k) savings program with an employer match and more