TRM Labs
Software Engineer, Data Infrastructure - AI Platform
About this role
Join TRM Labs' Data Platform Serving team to own the StarRocks-backed infrastructure powering government cloud investigations. You'll perform query optimization, harden data pipelines, and become the second operator of a mission-critical OLAP serving layer in a regulated, high-availability environment.
What you'll do
- Tune StarRocks serving layer performance using AI-assisted query profiling to prevent customer-facing incidents
- Build and harden data pipelines feeding government cloud investigations with AI code review workflows
- Reduce single-point-of-failure risk by becoming the second engineer capable of independently operating and troubleshooting the serving layer
- Triage production issues using AI-assisted debugging and log analysis in a regulated environment
- Participate in on-call rotation with incident response and postmortem responsibilities
- Collaborate with Forward Deployed Engineering and Product teams to maintain platform parity
What they're looking for
- Distributed OLAP or serving-layer systems (StarRocks, Trino, ClickHouse, or similar)
- Query tuning and performance optimization at scale
- Data pipeline reliability and incident response
- AI-assisted tools (Claude, Cursor) for debugging and code review
- Production infrastructure troubleshooting and ramping
- On-call operations and independent ownership
- Compliance and regulated environment experience
- Distributed systems and database migration knowledge
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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
- Tell us about your experience operating distributed OLAP systems—what performance bottlenecks have you debugged, and how did you resolve them?
- How do you approach ramping on unfamiliar production infrastructure, and can you walk through a recent example?