Software Engineer, Data Systems

Eventual•San Francisco, CA

4d•Onsite

About The Position

Eventual is building a new kind of data platform for Physical AI, designed to handle massive multimodal datasets like video, lidar, and sensor data, which traditional platforms like Databricks and Snowflake are not optimized for. The company's open-source engine, Daft, is specifically built for multimodal AI, processing petabytes of data daily. Eventual's infrastructure enables users to find any situation across a fleet's video history, creating training sets or alerts. The team, founded in 2022, has raised $30M and comprises experienced professionals from companies like AWS, Lyft, and Tesla. They are looking for engineers to join their small, powerful team, working 4 days a week in their SF Mission District office. Their mission is to build Scenario Mining and Data Curation for robot fleet data, empowering Physical AI and robotics teams to instantly find, curate, and stream data for training frontier models. Eventual operates as an agile team where engineers have high ownership across the stack, from compute infrastructure to data storage, querying, and model training/deployment.

Requirements

Proven track record building resilient, high-throughput distributed systems or database engines using Rust or C++.
3+ years of experience diving deep into engine internals—such as vectorized execution, query planning/optimization, distributed task scheduling, or zero-copy networking.
High agency and adaptability to thrive in an autonomous, fast-paced startup environment building cutting-edge infrastructure for frontier robotics.

Nice To Haves

Practical exposure to scaling cloud infrastructure (AWS S3) and managing heavy-compute data pipelines.
Experience with CUDA, GPU streaming, or video decoding frameworks.

Responsibilities

Build key capabilities for Eventual's storage and high-throughput data engine over petabytes of video, lidar and high-frequency telemetry data.
Work directly on the architecture powering real-time indexing of perception/robotics data, distributed storage/compute, and dataloading at line-rate to GPUs for model training and inference.
Build Multimodal Storage (Data Lake) against modern columnar data lake formats (Apache Parquet, Apache Iceberg etc) optimized for high-dimensional video, lidar and sensor logs.
Build powerful querying capabilities for multi-stage query systems, including partitioning, indexing, query planning, embeddings/vector search, and LLMs/VLMs for perception-based query predicates.
Improve memory stability, throughput, and zero-copy data flow through streaming computation and line-rate CUDA tensor delivery to GPUs for dataloading.