Senior Data Scientist — Graph & Knowledge ML

Technology Resource Management; United States; A distancia; USD 180.000 - 210.000; Jornada completa

Descripción de la vacante

TRM Labs in the United States is seeking a Full Stack Data Scientist / Machine Learning Engineer to join our Knowledge Layer team, focused on extracting, structuring, and analyzing knowledge from large-scale unstructured data. You will bridge ML and graph engineering, turning research into production-ready services.

You will own ML components end-to-end—from model selection and experimentation to deployment and API integration—while collaborating with backend and graph engineers.

Formación

• 5+ years of experience in data science, ML engineering, or applied ML.
• Strong Python programming skills with production ML experience.
• Familiarity with NLP or information extraction techniques (NER, text classification, embeddings).
• Interest in knowledge graphs, graph data, or graph-based ML.
• Strong software engineering fundamentals; ability to build APIs/services and ship ML components end-to-end.

Responsabilidades

• Design, build, and productionize ML models focused on knowledge extraction from unstructured data (NER, entity linking).
• Graph-based learning and inference; entity resolution and relationship discovery.
• Evaluate and leverage existing ML models and frameworks to solve real-world problems efficiently.
• Partner with backend and graph engineers to integrate ML models into production services and APIs.
• Own ML components end-to-end, including experimentation, evaluation, deployment, and iteration.
• Help shape best practices for applied ML within the Knowledge Layer team.

Conocimientos

5+ years experience

Python

NLP

Graph ML

APIs

Communication

Herramientas

Python tooling

ML frameworks

APIs

Descripción del empleo

TRM Labs in the United States is seeking a Full Stack Data Scientist / Machine Learning Engineer to join our Knowledge Layer team, focused on extracting, structuring, and analyzing knowledge from large-scale unstructured data. You will bridge ML and graph engineering, turning research into production-ready services.

You will own ML components end-to-end—from model selection and experimentation to deployment and API integration—while collaborating with backend and graph engineers.