Key skills
Machine learningNatural language processingPythonNumPyHugging FacePyTorchspaCyLarge language modelsFoundation modelsDeep learningModel trainingModel fine-tuningModel quantizationModel evaluationSparse data techniquesData augmentationWeak supervisionSemi-supervised learningTokenizationEmbeddingsAttention mechanismsTransformer architecturesData evaluationModel explainabilityError analysisLinuxMachine LearningMLDeep LearningNLPNatural Language ProcessingGenAI
About this role
Point72 is a leading global alternative investment firm that seeks to deliver superior returns through fundamental and systematic investing strategies. They are looking for a Machine Learning Engineer specializing in natural language processing to develop algorithmic solutions and models that support investment professionals, while collaborating with cross-functional teams to integrate these models into production systems.
Responsibilities:
- 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
Requirements:
- 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