Blockchain intelligence platform for detecting crypto financial crime
TRM Labs builds blockchain intelligence to help financial institutions, crypto businesses, and government agencies detect and investigate digital-asset fraud. The tech stack reveals a data-intensive operation: Python + Apache Spark/Airflow/Flink + BigQuery/Snowflake + Kafka, paired with machine-learning infrastructure (NVIDIA GPUs, TensorRT, vLLM, FlashAttention). Active hiring skews heavily toward security (46 roles) over engineering (26), signaling a shift from building toward compliance and threat operations—confirmed by projects targeting proactive on-chain threat hunting, low-latency blockchain queries, and agency procurement.
Notable leadership hires: Sales Account Director
TRM Labs is a blockchain intelligence company founded in 2018 and headquartered in San Francisco. The platform serves financial institutions, crypto exchanges, and government agencies with detection, monitoring, and forensic tools for crypto-related financial crime, sanctions compliance, and transaction monitoring. The company operates at scale across data engineering, data science, and threat intelligence, with active hiring in 16+ countries including the US, Europe, Asia-Pacific, and Africa. Current project focus includes expanding government-sector go-to-market, blockchain data integration services, and deploying AI-driven threat hunting capabilities.
Python, Apache Spark, Airflow, Flink, dbt, BigQuery, Snowflake, ClickHouse, Kubernetes, Kafka, PostgreSQL, React, NVIDIA GPUs, TensorRT, vLLM, Datadog, and Terraform.
San Francisco, California. The company was founded in 2018 and is privately held with 201–500 employees.
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TRM Labs's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.