Machine Learning Engineer, Knowledge Graph Intelligence

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.

• 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.

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

• Fully-paid health care benefits
• Generous parental and family leave policies
• Mental and physical wellness programs
• Volunteer opportunities
• Non-profit matching gift program
• Support for employee-led affinity groups representing women, minorities and the LGBT+ community
• Tuition assistance
• A 401(k) savings program with an employer match and more

Point72 Asset Management is a global firm led by Steven Cohen that invests in multiple asset classes and strategies worldwide. Resting on more than a quarter-century of investing experience, we seek to be the industry’s premier asset manager through delivering superior risk-adjusted returns, adhering to the highest ethical standards, and offering the greatest opportunities to the industry’s brightest talent. We’re inventing the future of finance by revolutionizing how we develop our people and how we use data to shape our thinking. For more information, visit www.Point72.com/working-here