Point72
Financial Services
MachineLearningEngineer,KnowledgeGraphIntelligence
Neural analysis suggests this role is
optimal for Senior candidates.
“Machine Learning Engineer, Knowledge Graph Intelligence at Point72. Skills: Machine Learning, Natural Language Processing, Deep Learning. Develop algorithmic solutions. Develop models for production”
Industry & Context.
Improve model accuracy; Improve model generalization; Data evaluation; Model explainability; Error analysis
Linux environment experience
What They're Looking For.
Must Have
PhD, master's degree, or 4+ years experience, 6+ years building ML models, Python proficiency, NumPy experience, Hugging Face experience, PyTorch experience, spaCy experience, LLMs experience, Foundation models experience, Large-scale deep learning experience, Modern training experience, Fine-tuning experience, Quantization experience, Model evaluation experience, Sparse data expertise, Data augmentation techniques, Weak supervision techniques, Semi-supervised learning techniques, NLP concepts knowledge, Tokenization knowledge, Embeddings knowledge, Attention mechanisms knowledge, Transformer architectures knowledge, Data evaluation techniques experience, Model explainability experience, Error analysis experience, Linux environment experience, Commitment to ethical standards
Nice to Have
GCP Professional Data Engineer, AWS Data Analytics, Databricks Certified, dbt Certified
What You'll Do.
Develop algorithmic solutions
Develop models for production
Specialize in NLP solutions
Extract insights from text
Manage research process
Conduct novel research
Contribute to ML disciplines
Implement GenAI solutions
Utilize ML infrastructure
Contribute to modeling
Contribute to data preparation
Contribute to optimization
Contribute to performance enhancements
Work with sparse data
Improve model accuracy
Improve model generalization
Conduct data evaluation
Perform data preprocessing
Perform feature engineering
Perform model performance assessment
Collaborate cross-functionally
Integrate models into production
Stay up to date with NLP
Stay up to date with ML
How You'll Work.
Team & Collaboration
Cross-functionally with data engineers; Cross-functionally with software developers; Cross-functionally with product teams
Full Job Description
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. 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. Cond
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