About the job
Contribute to a Safer World.
At TRM Labs, we leverage blockchain analytics and AI innovations to empower law enforcement, national security entities, financial institutions, and cryptocurrency enterprises in identifying, investigating, and thwarting crypto-related fraud and financial misconduct. Our advanced blockchain intelligence and AI solutions facilitate the tracing of fund sources and destinations, recognition of illicit activities, case-building, and the development of a comprehensive threat landscape. We are a trusted partner to prominent organizations globally, dedicated to fostering a safer and more secure environment for all.
Our AI Engineering Team is focused on pioneering next-generation AI applications, with a particular emphasis on Large Language Models (LLMs) and agentic systems. Our goal is to create resilient pipelines, high-performance infrastructure, and operational tools that ensure AI systems can be deployed with speed, safety, and scalability.
We handle petabyte-scale data pipelines, deliver models with millisecond-level latency, and ensure the observability and governance necessary for AI to be production-ready. Our team is actively engaged in assessing and integrating state-of-the-art tools in the LLM and agent ecosystem, including open-source frameworks, vector databases, evaluation methodologies, and orchestration tools that empower TRM to innovate more rapidly than market demands.
Your Impact:
Design and implement a robust agentic framework that facilitates tool usage, context retrieval, memory integration, and strategic planning.
Develop intelligent, modular agents that automate investigative workflows and enhance analyst decision-making capabilities.
Expand and optimize our LLM infrastructure (e.g., OpenAI, Anthropic, on-premise models), including prompt engineering, retrieval-augmented generation, and user feedback loops.
Create safe, observable, and auditable agent behaviors, ensuring reliability in sensitive operational environments.
Assess performance based on metrics such as reasoning efficacy, latency, success rates, and hallucination instances, iterating based on user insights and system telemetry.
Foster a culture of ownership, rapid experimentation, and ethical AI deployment.
What We Seek:
Proven engineering expertise with extensive experience in backend or systems development (Python preferred).
Practical experience in building with LLMs, agent systems, and tooling frameworks (LangChain, semantic caches, vector databases, etc.).
Strong understanding of AI principles, particularly in relation to agentic systems and LLMs.

