About the role

Trellis is hiring founding backend engineers

Trellis is building a Snowflake 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 database from the ground up.
You get the chance to be an early team member at a YC-backed startup spun-out from the Stanford AI lab.
You get to join a world-class team (e.g., team members have previously won the international physics olympiad, published economics research, and taught AI classes to hundreds of Stanford graduate students).
You work with founders who are engineers, not business majors.
Extreme ownership: you will own products and products will live and die by the decisions you make and the work you do.

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:

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.)
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)
Enrich their data warehouse with business-critical information (e.g., Retrieving detailed pricing and quantity information of products sold on competitor websites)