NLP & Knowledge Graph ML Engineer

Point72; New York (NY); On-site; USD 175,000 - 250,000; Full time

Job summary

Point72 is seeking a Machine Learning Engineer - Applied Scientist to develop production-ready NLP solutions for front office investments. You will manage research, data collection, modeling, testing, and evaluation, applying NLP to extract insights from unstructured text and enabling predictive, clustering, and time-series analyses.

You will collaborate cross-functionally to integrate models into production and stay updated on NLP and ML advances, with a focus on responsible, ethical AI

Qualifications

• PhD, master's degree, or 4+ years of CS/CE/ML experience.
• 6+ years building ML models and algorithms.
• Proficient in Python with NumPy, Hugging Face, PyTorch, spaCy for NLP.
• Experience with LLMs/foundation models and large-deep learning systems.
• Expertise with sparse data, data augmentation, weak/semi-supervised learning.
• Solid NLP knowledge: tokenization, embeddings, transformers.
• Linux experience and commitment to high ethics.

Responsibilities

• Contribute to NLP, unstructured data analysis, predictive modeling, and classic ML projects.
• Implement GenAI solutions and improve modeling, data prep, optimization, and performance.
• Work with sparse data to boost accuracy and generalization.
• Data evaluation: preprocessing, feature engineering, model assessment.
• Collaborate with data engineers, software developers, and product teams to productionize models.
• Stay current with NLP/ML advances and apply new techniques as needed.

Skills

Python

NumPy

Hugging Face

PyTorch

spaCy

NLP

LLMs

foundation models

data preprocessing

model evaluation

Linux

Education

PhD or Master’s degree or 4+ years of related field experience

Tools

Linux

Job description

Point72 is seeking a Machine Learning Engineer - Applied Scientist to develop production-ready NLP solutions for front office investments. You will manage research, data collection, modeling, testing, and evaluation, applying NLP to extract insights from unstructured text and enabling predictive, clustering, and time-series analyses.

You will collaborate cross-functionally to integrate models into production and stay updated on NLP and ML advances, with a focus on responsible, ethical AI