TRM Labs
Data Engineer - AI/ML
About this role
TRM Labs seeks a Data Engineer to join the Data Platform Serving team, operating and scaling a StarRocks-backed serving layer for government cloud investigations. You'll own performance tuning, pipeline reliability, and incident response in a high-compliance environment, working alongside the current solo owner to reduce single points of failure.
What you'll do
- Tune and optimize StarRocks query performance using AI-assisted profiling to prevent customer-facing incidents
- Build and harden data pipelines feeding government cloud investigations with AI code review workflows
- Become the second operator capable of independently troubleshooting the serving layer and reducing incident response time
- Perform AI-assisted debugging and log analysis to triage production issues in regulated environments
- Own data pipeline reliability and incident response with on-call responsibilities
- Participate in weekly syncs, sprint planning, and post-incident retros to improve infrastructure and runbooks
What they're looking for
- Distributed OLAP or serving-layer systems (StarRocks, Trino, ClickHouse preferred)
- Query tuning and performance optimization at scale
- Data pipeline design and reliability engineering
- Production incident response and troubleshooting
- AI-assisted development tools (Claude, Cursor, or similar)
- Async documentation and communication
- Independent ownership and self-directed ramp-up on unfamiliar systems
- Government compliance and regulated environment experience
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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 debugged a complex production performance issue in a distributed system—what was your process and how did you isolate root cause?
- How have you used AI tools like Claude or Cursor to accelerate debugging or code review in past projects, and what patterns worked best?