You’ll build and evolve the data systems that power Cursor’s product and internal decision-making. The work is hands-on and high-impact.
This includes:
Designing and operating large-scale batch data systems using Spark and Ray Data
Scaling data ingestion pipelines as we grow to billions of rows per day
Re-architecting prompt and model interaction data storage with a focus on cost, performance, and usability, primarily on S3
Building and maintaining streaming data infrastructure (Kafka, Flink, or similar)
Working across data warehouses and lakehouse formats, including Iceberg and Delta Lake (or lower-level storage abstractions)
Improving data developer experience, especially for Python-heavy workflows
Supporting database replication and change data capture pipelines (DMS, Debezium, or similar)
We’re looking for someone who has built real systems at scale and cares about correctness, cost, and ergonomics.
Strong signals include:
Deep experience with Spark (Databricks or open-source Spark both count)
Production experience with Ray Data
Hands-on ownership of large data pipelines and storage systems
Comfort debugging performance issues across compute, storage, and networking layers
Clear thinking about data modeling and long-term maintainability
Nice to have
Experience running or scaling ClickHouse
Familiarity with dbt, Dagster, or similar orchestration and modeling tools
Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering.
Our organization is very flat, and our team is small and talent dense. We particularly like people who are truthseeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
We're in-person with cozy offices in North Beach, San Francisco and Manhattan, New York, replete with well-stocked libraries.
If there appears to be a fit, we'll reach to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.