Full Stack Data Scientist / Machine Learning Engineer (Knowledge Graphs)

Location: Remote in North America and the United States; participation in in-person R&D work weeks typically 1–2 times per quarter at a TRM hub or other designated location, with additional travel possible.

Base salary: $180,000–$210,000 annually in the United States. The role may also be eligible for TRM’s equity plan.

Company

hirify.global provides AI-powered blockchain intelligence solutions that help public and private sector agencies investigate illicit activity, build cases, and disrupt crime.

What you will do

• Design, build, evaluate, and productionize machine learning models for knowledge extraction from unstructured data, including NER and entity linking.
• Develop graph-based learning and inference solutions for 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.
• Contribute to the design and evolution of knowledge graphs and ontologies, using exploratory data analysis to inform modeling and system design.
• Own ML components end-to-end and help establish applied ML best practices within the Knowledge Layer team.

Requirements

• 5+ years of experience in data science, machine learning engineering, or applied machine learning.
• Strong programming experience in Python.
• Hands-on experience building, training, or deploying machine learning models in production.
• Familiarity with NLP or information extraction techniques, including Named Entity Recognition, text classification, or embedding-based approaches.
• Experience or strong interest in knowledge graphs, graph data, or graph-based machine learning.
• Solid software engineering fundamentals, including building and maintaining APIs or services.
• Ability to translate ambiguous problem spaces into practical ML solutions.
• Strong communication skills and comfort collaborating with engineers across disciplines.
• AI fluency is expected, including applying AI to accelerate workflows, structure and solve problems, improve output quality, and increase speed and leverage.

Culture & Benefits

• Distributed-first work environment with hubs in Los Angeles, San Francisco, New York, Washington D.C., London, and Singapore.
• Approved travel, lodging, and workspace for required R&D work weeks are coordinated and covered.
• Accommodations are available for legitimate caregiving, health, immigration, and similar constraints.
• High-ownership environment focused on speed, experimentation, continuous feedback, collaboration, and measurable impact.
• Work centers on complex problems at the intersection of AI, national security, blockchain intelligence, and fighting crime.

Hiring process

• Recruiter introduction, hiring manager discussion, and first-round interviews focused on critical skills.
• Final hands-on case study, typically in a panel-style session, plus an AI skills assessment and Leadership Principles interview.
• References followed by an offer and compensation discussion.