TRM Labs
Data Platform Engineer - AI Platform
About this role
TRM Labs is seeking a Data Platform Engineer to build and operate the StarRocks-backed serving layer for government cloud investigations. You'll become the second engineer owning this high-availability distributed system, using AI-assisted tools to optimize queries, harden pipelines, and rapidly triage production issues in a compliance-heavy environment.
What you'll do
- Own performance tuning and query optimization on the StarRocks serving layer using AI-assisted profiling
- Build and harden data pipelines feeding government cloud investigations with compliance requirements
- Reduce single-point-of-failure risk by becoming the second operator of the distributed serving infrastructure
- Triage and resolve production incidents using AI-assisted debugging and log analysis
- Participate in weekly syncs and incident retros to improve runbooks and system reliability
- Independently ramp on unfamiliar production infrastructure and take on-call responsibility
What they're looking for
- StarRocks, Trino, ClickHouse, or similar distributed OLAP/serving-layer systems
- Query tuning and performance optimization at scale
- Data pipeline reliability and incident response
- AI tools (Claude, Cursor, or similar) for debugging and code review
- Production systems troubleshooting and root-cause analysis
- GovCloud or regulated environment experience (preferred)
- Async documentation and communication
- On-call operations and independent ownership
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TRM Labs
TRM Labs builds AI-powered intelligence products designed to detect and disrupt financial crime through advanced machine learning and investigative tools. The company is hiring backend engineers, ML infrastructure engineers, AI research engineers, and AI agent engineers to develop scalable platform infrastructure, GPU-backed systems, model optimization capabilities, and LLM-based agentic applications.
- Website
- trmlabs.com
Likely interview questions
- Walk us through a time you optimized slow queries in a distributed database—what tools did you use and how did you measure improvement?
- Describe your experience with data pipeline incident response. How would you approach triaging a production outage in a system you're unfamiliar with?