- Location
- Anywhere in the US
- Workplace
- Remote
- Type
- Full-time
- Department
- Engineering
- Source
- Lever
Description
Trellis (https://trellis.law/) is the largest legal research platform for state trial courts, with coverage spanning thousands of courts and millions of cases across the U.S. We aggregate and structure trial court filings, rulings, motions, and outcomes into a unified, searchable platform, combining large-scale data infrastructure with AI-powered analysis to extract, organize, and surface insights across that data. This makes large-scale trial court research possible for the first time.
Our tools enable attorneys to analyze judge tendencies, evaluate opposing counsel, find and assess expert witnesses, and see which arguments and strategies have succeeded in similar cases, so they can assess risk earlier, save time on research, and develop more effective, data-backed case strategies.
We’re revolutionizing the way legal professionals access and use trial court data to make informed decisions. As we scale, we’re looking for a thoughtful and execution-focused Product Designer to help shape the future of our product experience.
About the Role:
We're looking for a QA Engineer who approaches quality as an engineering problem. This is NOT just a manual testing role — it's for an engineer who writes automation to proactively find bugs, surface data quality issues at scale, and then writes or contributes code to fix them.
You'll work closely with engineering and product to build systems that continuously monitor data integrity and product behavior, surface issues based on business impact, and drive resolution. The ideal candidate is a strong programmer who is comfortable working across large datasets, debugging complex pipelines, and owning quality end-to-end: from detection to fix.
We want someone who thinks like a software engineer first and a quality advocate second: someone who sees a data anomaly or product bug and immediately thinks "how do I build something that catches this automatically — and then fixes it?" You should be energized by ambiguity, comfortable digging into large datasets and production logs, and able to move quickly without waiting to be handed a spec.
Our tech stack includes Python, Django, Postgres, Elasticsearch, Vue, vanilla JavaScript, and AWS.
