Founding Engineer

Trellis is building a Snowflake for unstructured data, turning unstructured data (e.g., financial documents, insurance policies, chat logs, etc.) into SQL-compliant tables. Trellis is a spinout from Stanford AI lab and is backed by leading investors including YC, General Catalyst, Telesoft partners, and executives at Google and Salesforce.

• 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.

• 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.

• Become the main owner of Trellis's backend microservices and infrastructure.
• Design, build, and test new products and features from end-to-end.
• Write high-quality, scalable Python code. Work with major open-source tools, including Kubernetes, Docker, and Airflow, to manage deployment on cloud providers. Support enterprise customer deployments.
• You will work closely with the founders to lay the technical and operational foundation of Trellis engineering team.
• Architect and implement complex data pipelines and database systems (SQL/No-SQL).
• Experience with ML infrastructure and LLM training is a big plus.