Software Engineer, Large-Scale Data Query Systems

Eventual•San Francisco, CA

2d•$150,000 - $250,000•Onsite

About The Position

Eventual is building a new type of data platform designed for the demands of Physical AI, which requires processing massive amounts of multimodal data (video, lidar, radar, sensor data). Current platforms are not optimized for this, leading to significant time loss in data loading. Eventual's open-source engine, Daft, is purpose-built for multimodal AI and is already handling petabytes of data daily at major companies. The company is developing a video-native index to stream curated datasets to GPUs at high speeds, aiming to support future hardware advancements. Eventual has raised $30M from prominent investors and has a team with experience from leading tech companies in the self-driving and AI sectors. The role is based in their San Francisco office, with a 4-day in-office work week.

Requirements

Strong foundation in systems programming.
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).
Works well in small, focused teams with fast iterations and lots of autonomy.
Passionate, intellectually curious and excited to build the next generation of distributed data technologies.

Responsibilities

Build key capabilities for the Daft distributed data engine.
Work on core architectural design and implementation of various components in Daft.
Intelligently optimize users’ workloads with modern database techniques (Planning/Query Optimizer).
Improve memory stability through the use of streaming computation and more efficient data structures (Execution Engine).
Improve Daft’s resource utilization, task scheduling and fault tolerance (Distributed Scheduler).
Improve Daft integrations with modern data lake technologies such as Apache Parquet, Apache Iceberg and Delta Lake (Storage).
Tinker with infrastructure, talk to customers and participate heavily in the core design process of the product.