Trellis is hiring 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. 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. What you will do 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.