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# Software Engineer, Systems

- **Company:** EVENTUAL INC.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon S3, Apache HTTP Server, BigQuery, C++ (Programming Language), Cloud Computing, Databases, Information Engineering, Data Structures, Linux, Distributed Data Store, Fault Tolerance, Apache Hadoop, PostgreSQL, Open Source Technology, Query Optimization, System Programming, Parquet, Apache Spark, Data Lakes, Dask
- **Published:** July 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=dde153ff40b926ee

## About the Role

* We are looking for a candidate with a strong foundation in systems programming and ideally experience with building distributed data systems or databases (e.g. Hadoop, Spark, Dask, Ray, BigQuery, PostgreSQL etc)
 * 3+ years of experience working with distributed data systems (query planning, optimizations, workload pipelining, scheduling, networking, fault tolerance etc)
 * Strong fundamentals in systems programming (e.g. C++, Rust, C) and Linux
 * Familiarity and experience with cloud technologies (e.g. AWS S3 etc)
 * Most importantly, we are looking for someone who works well in small, focused teams with fast iterations and lots of autonomy. If you are passionate, intellectually curious and excited to build the next generation of distributed data technologies, we want you on the team!

## Description

As a Software Engineer on the Systems team, you will build key capabilities for the Daft distributed data engine. You will be working on core architectural design and implementation of various components in Daft. While we are an experienced team that can provide constant guidance and mentorship, we value engineers who can autonomously scope and solve difficult technical challenges., * Planning/Query Optimizer: intelligently optimize users' workloads with modern database techniques

 * Execution Engine: improve memory stability through the use of streaming computation and more efficient data structures
 * Distributed Scheduler: improve Daft's resource utilization, task scheduling and fault tolerance
 * Storage: improve Daft integrations with modern data lake technologies such as Apache Parquet, Apache Iceberg and Delta Lake
 * Our goal is to build the world's best open-source distributed query engine, becoming the leading framework for data engineering and analytics.
 * We are a young startup - so be prepared to wear many hats such as tinkering with infrastructure, talking to customers and participating heavily in the core design process of our product!


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