Senior ML Engineer: Knowledge Graphs & NLP (Equity)

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

Descripción de la vacante

TRM Labs is seeking a Full Stack Data Scientist / Machine Learning Engineer to join our Knowledge Layer team. You will drive practical ML in knowledge extraction, graph-based learning, and entity resolution, partnering with backend and graph engineers to deploy models as scalable APIs.

You will own end-to-end ML components from experimentation to deployment. The role emphasizes building, evaluating, and improving ML systems in production, with a focus on graph analytics and knowledge graphs,

Formación

• 5+ years of experience in data science, machine learning engineering, or applied ML.
• Strong programming experience in Python.
• Hands-on experience building, training, or deploying machine learning models in production.
• Familiarity with NLP or information extraction techniques (NER, text classification, embeddings).
• Experience or strong interest in knowledge graphs, graph data, or graph-based ML.

Responsabilidades

• Design, build, and productionize ML models focused on knowledge extraction, graph-based learning, and entity resolution.
• Evaluate and leverage existing ML models and frameworks to solve real-world problems efficiently.
• Collaborate with backend and graph engineers to integrate ML models into production services and APIs.
• Contribute to design and evolution of knowledge graphs and ontologies.
• Own ML components end-to-end including experimentation, evaluation, deployment, and iteration.

Conocimientos

Python

ML production

NLP / information extraction

Knowledge graphs

APIs

Descripción del empleo

TRM Labs is seeking a Full Stack Data Scientist / Machine Learning Engineer to join our Knowledge Layer team. You will drive practical ML in knowledge extraction, graph-based learning, and entity resolution, partnering with backend and graph engineers to deploy models as scalable APIs.

You will own end-to-end ML components from experimentation to deployment. The role emphasizes building, evaluating, and improving ML systems in production, with a focus on graph analytics and knowledge graphs,