Interested in this Data Engineer role at TRM Labs?
Apply Now →Skills & Technologies
About This Role
Build a Safer World.
------------------------
TRM Labs provides AI\-powered intelligence solutions that help public and private sector agencies investigate and disrupt crime. TRM's platforms enable investigators to trace illicit activity, build cases, and construct operating pictures of threat networks. Leading agencies and businesses worldwide rely on TRM to make the world safer and more secure.
TRM's government cloud environment gives public sector investigators the same real\-time analytical power our commercial customers rely on, and this role keeps that environment fast, reliable, and compliant as it scales. You'll join the Data Platform Serving team, working on the StarRocks\-backed serving layer that sits underneath government cloud investigations, alongside the engineer who currently owns this domain solo. This is a chance to build hands\-on distributed systems experience in one of the most operationally demanding environments at TRM: a regulated, high\-availability government cloud deployment.
The impact you will have:
- You will own performance tuning on the StarRocks serving layer, using AI\-assisted query profiling (Claude, internal tooling) to find and fix slow query patterns before they become customer\-facing incidents.
- You will build and harden data pipelines feeding government cloud investigations, using AI code review workflows to ship reliable changes faster in a high\-compliance environment where mistakes are costly.
- You will reduce single\-point\-of\-failure risk on GovCloud data infrastructure by becoming the second engineer who can independently operate and troubleshoot the serving layer, cutting incident response time when the primary owner is unavailable.
- You will use AI\-assisted debugging and log analysis to triage production issues in a regulated environment, turning multi\-hour investigations into rapid root\-cause fixes.
What we’re looking for:
- U.S. citizenship is required for this role due to government cloud data access requirements.
- Hands\-on experience operating distributed OLAP or serving\-layer systems (StarRocks, Trino, ClickHouse, or similar), including query tuning and performance optimization at scale.
- Experience owning data pipeline reliability and incident response, and comfort using AI tools (Claude, Cursor, or similar) to accelerate debugging, code review, and documentation.
- Independent ownership mindset: you can pick up an unfamiliar piece of production infrastructure, use AI\-assisted research and code exploration to ramp quickly, and take on\-call responsibility with minimal oversight.
About the Team:
- The Data Platform Serving team owns the layer that turns TRM's data into fast, reliable answers for the teams and systems built on top of it.
- We're distributed, not distant: the team communicates constantly across Slack and async docs, with a bias toward direct, evidence\-based technical discussion.
- Decisions are made close to the data: engineers who operate the systems make the calls on architecture and tradeoffs, with input from the broader Data Platform org.
- We work closely with Forward Deployed Engineering and Product teams to keep the government cloud environment at parity with our commercial platform.
Team Operating Rhythms:
- Weekly team sync to review open incidents, in\-flight infrastructure work, and upcoming compliance milestones.
- Async daily updates in Slack on pipeline health, ongoing tickets, and blockers.
- Sprint\-based planning cycles with clear ownership assigned per workstream.
- Retro after any production incident to capture learnings and adjust runbooks.
Learn about TRM Speed in this position:
- A production database migration broke search\-attribute registration across two environments right before a critical audit deadline. The on\-call engineer traced the root cause, shipped a fix, and had both environments passing smoke tests again within the same day.
- With a compliance deadline days away, the team needed off\-cluster backups for the government cloud database with zero prior tooling in place. An engineer designed and shipped the full backup and restore pipeline — three stacked PRs — in under two weeks.
- Audit log retention needed to jump to meet a new compliance floor with no advance notice. The engineer used AI\-assisted config generation to update retention policies across the environment and verify compliance the same week.
### Life at TRM
We are building a safer world. That promise shows up in how we work every day.
TRM moves quickly. We are a high velocity, high ownership team that expects clarity, follow\-through, and impact. People who thrive here are energized by hard problems, experimentation, and continuous feedback. If something takes months elsewhere, it will ship here in days.
Our work sits at the intersection of AI, national security, and fighting crime. The problems are complex, the stakes are real, and the environment evolves quickly. The pace and intensity of the work reflect the importance of the mission. As a result, the way we operate requires a high level of ownership, adaptability, collaboration, and creative problem\-solving.
At TRM, you should expect:
- Priorities and targets to change quickly as we experiment and iterate
- Work that often requires operating with a high degree of ambiguity
- A high level of personal ownership and accountability
- Close collaboration across teams and functions
- Frequent, high\-touch communication
- Creative problem solving and out\-of\-the\-box thinking
- A pace that rewards urgency, adaptability, and outcomes
This environment is energizing for people who enjoy building, solving hard problems, and making progress in situations that are not always fully defined. It also requires comfort navigating ambiguity, adjusting course as new information emerges, and maintaining focus and positivity in a fast\-moving and intense environment.
We also recognize that this style of operating is not for everyone. If you are primarily optimizing for predictability or a consistently balanced workload, we encourage you to use the interview process to pressure test whether this environment is truly the right fit. We want teammates who thrive here, not just survive here.
At the same time, many people find this work deeply rewarding. If you are excited by meaningful problems, motivated by ambitious goals, and energized by working alongside mission\-driven colleagues, there is a good chance you will find TRM to be an exceptional place to grow and contribute. Learn more: Interviewing at TRM: How We Hire and What Success Looks Like
### AI Fluency at TRM
AI fluency is a baseline expectation at TRM.
We believe AI meaningfully changes how top performers operate. We expect every team member to use AI to accelerate and reimagine their craft, not just automate surface tasks.
At TRM, AI fluency means you are among the top 10 percent of operators in your function in how you apply AI to:
- Accelerate repeatable workflows
- Structure and solve problems
- Improve output quality
- Increase speed and leverage
You will be evaluated on applied AI fluency during the interview process.
### Leadership Principles
We hire and grow against three leadership principles. They’re the standards for how we operate, treat each other, and make decisions.
- Impact\-Oriented Trailblazer: We put customers first and move with speed, focus, and adaptability. We treat every plan like an experiment – test, ship, measure, and iterate quickly.
- Master Craftsperson: We care deeply about our craft. We balance speed with high standards, own outcomes end‑to‑end, and invest in getting better everyday.
- Inspiring Colleague: We add clarity and energy, not noise. We bring humility, candor, and a one‑team mindset — giving and receiving feedback to make the team stronger.
### Join our Mission
At TRM we care deeply about our craft. We are looking for individuals who want their work to matter, who experiment with speed and rigor, and who take pride in building a safer world for billions of people. If you’re excited by TRM’s mission but don’t check every box, we encourage you to apply — we hire for slope, judgment, and the will to learn fast.
TRM is a Series C company with $220M in total funding, backed by Goldman Sachs, Bessemer, Y Combinator, Thoma Bravo, and others. Headquartered in San Francisco, TRM operates as a distributed\-first company with hubs in Los Angeles, San Francisco, New York, Washington D.C., London, and Singapore.
### Privacy Policy and Additional Information
By submitting your application, you agree to allow TRM Labs to process your personal information in accordance with our Privacy Policy.
We collect the information you provide (such as your resume, work history, and contact details) solely for the purpose of evaluating your candidacy for current and future roles at TRM.
Because our hiring cycles for certain positions may span 24 to 36 months, we retain your personal information for up to 36 months from the date of your application. After that period, your data is deleted unless a different retention period is required or permitted by law.
If you are located in the European Economic Area, the United Kingdom, or another jurisdiction with applicable data protection laws, you have the right to access, correct, or request deletion of your personal data at any time before that period ends. To exercise any of these rights, contact us at [email protected].
To notify TRM Labs that you believe this job posting is non\-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.
The use of AI tools of any kind (including but not limited to notetakers, interview assistants, and real\-time coaching tools such as Otter.ai, Fireflies, Fathom, Cluey, or similar) during TRM interviews is not permitted without prior approval from TRM. TRM uses its own internal tools for note\-taking to ensure a consistent and confidential experience for all candidates.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this form.
### Recruitment agencies
TRM Labs does not accept unsolicited agency resumes. Please do not forward resumes to TRM employees. TRM Labs is not responsible for any fees related to unsolicited resumes and will not pay fees to any third\-party agency or company without a signed agreement.
### Learn More: Company Values \| Interviewing \| FAQs
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At TRM Labs, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
TRM Labs AI Hiring
TRM Labs has 8 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, AI Software Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.