Founding Engineer (Backend, AI)

Trellis is building an AI-powered workflow for unstructured data, turning unstructured data (e.g., financial documents, insurance policies, chat logs, etc.) into SQL-compliant tables. We're currently backed by YC, General Catalyst, and early investors/executives in Google, Salesforce, and JP Morgan Chase.

Why work with us? • Be at the forefront of what's possible in AI and Data infrastructure. Build a new data platform from the ground up while collaborating with F500 enterprise clients. • You get the chance to be an early team member at a YC/GC -backed startup spun-out from the Stanford AI lab. • Join a world-class team (e.g., team members have previously won the international physics olympiad, published economics research, and taught graduate-level Big Data systems class at Stanford). • You are passionate about building high quality software that have direct impact. • Extreme ownership: You enjoy wearing different hats and own new product end-to-end. • You work with founders who are engineers, not business majors.

Requirements: • Experience architecting, developing, and testing full-stack code end-to-end • Expertise in programming languages such as Python, Go and ML/NLP libraries such as PyTorch, Tensorflow, Transformers. • Being proactive and a fast-learner with bias for action. • Open source contributions and projects are a big plus. • Experience working with relational and non-relational databases, especially Postgres • Experience with data and ML infra • Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) is a plus.

About Trellis: Trellis converts your unstructured data into SQL-compliant tables with a schema you define in natural language. With Trellis, you can now run SQL queries on complex data sources like financial documents, contracts, and emails. Our AI engine guarantees accurate schema and results.

Leading enterprises use Trellis to:

1. Unlock hidden revenue in their customer data (e.g., Underwriting teams use Trellis to extract key features from transaction data and build better risk models.)
2. Supercharge RAG applications by enabling end-users to ask analytical questions not possible before with traditional Vector DB (e.g., what are the top three features that users are requesting)
3. Enrich their data warehouse with business-critical information (e.g., Retrieving detailed pricing and quantity information of products sold on competitor websites)