Research Engineer, Knowledge Graph Intelligence

Overview

Machine Learning Engineer - Applied Scientist role focused on developing production-ready ML/NLP solutions for investment professionals.

What you'll do

• Develop algorithmic solutions and models for production-ready applications.
• Create NLP solutions that extract insights from unstructured text.
• Manage the full research process from methodology selection through evaluation.
• Implement GenAI solutions using ML infrastructure and improve modeling/performance.
• Apply techniques for sparse data to improve accuracy and generalization.
• Evaluate data via preprocessing, feature engineering, and model performance assessment.
• Collaborate with data engineers, software developers, and product teams to integrate models into production.

What you'll need

• 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.
• Hands-on experience with NumPy, Hugging Face, PyTorch, and spaCy for NLP.
• Prior experience in LLMs, foundation models, or large-scale deep learning systems including training, fine-tuning, quantization, and model evaluation.
• Expertise working with sparse data using data augmentation, weak supervision, and semi-supervised learning.
• Experience working in a Linux environment.

Details

• Annual base salary range: $175,000-$250,000 (USD).