Build the Future of Stream Processing in Rust! at Materialize


We're looking to grow our team with 3-4 relatively senior engineers who can iterate efficiently, desire impact, demonstrate resourcefulness, and strike a balance between autonomy and collaboration. Our culture is still embryonic, and you can help shape it if you join as an early engineer.

  • You will solve challenging problems in distributed systems, infrastructure engineering, and compilers.

  • Our chief scientist is an award-winning computer science researcher, who helped pioneer the relevant research and implemented our technical foundation.

  • Our head of engineering has managed large teams of engineers (e.g. all of product engineering at YouTube), co-founded and led Dropbox’s NYC office, and significantly contributed to multiple companies that IPOed or have been acquired.

  • Join a well-funded company ($8.5 million Series A) with high potential upside and equity ownership

  • Our salary and equity are competitive relative to Bay Area Series A companies.

  • We're located in SoHo, in the heart of downtown New York City.

  • We’re building on significant, battle-tested Rust codebases: Timely Dataflow and Differential Dataflow.

Technical challenges for you to solve We have many challenges relating to distributed systems, algorithmic optimization, and compilers.

We are writing a SQL layer on top of Timely Dataflow and Differential Dataflow, two fast, well-tested systems originally created by our co-founder Frank McSherry. To do this, we need a SQL parser, SQL type system, and the ability to perform various SQL-related operations efficiently. In addition, we'll need to create a means to easily to deploy our software on a cluster, as well as run multiple Timely clusters in active-active settings. Later, there will be substantial challenges in other areas such as query optimization, message durability, and checkpointing and fast replay.

We have begun writing a SQL parser and hooking it up to an execution engine to allow users to create SQL materialized views that are backed by data flows that incrementally update with very low latency. But there's plenty more to be done. The SQL engine will need a type system. We'll need to an easy way to create and monitor a high-availability deployment of Materialize. This will likely involve container orchestration or some other form of automated deployment. We'll need extensive performance testing and optimization.