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About the Role
As a Storage Infrastructure Engineer, you will apply modern database principles to Physical AI data. Your focus will be on optimizing the speed of our multimodal warehouse, which co-indexes video, sensors, embeddings, and sim outputs. This involves designing efficient indices, implementing predicate pushdowns, optimizing file formats for random access, and ensuring minimal data reads for queries.
Responsibilities
- Design and build the storage and indexing layer, including row groups, column chunks, secondary indices, vector indices, and metadata for efficient querying.
- Enhance the query engine through predicate pushdown, projection pushdown, and late materialization across multimodal columns.
- Select, extend, or develop modern open formats like Parquet, Iceberg, or Delta, and contribute to upstream projects.
- Develop versioning and schema evolution for multimodal datasets to ensure data reproducibility.
- Collaborate with the Dataloading team to minimize data read from NVMe.
- Integrate model outputs into the index without an external glue layer by partnering with the Visual Understanding team.
Requirements
- Strong understanding of indices (B+ trees, LSM trees, bitmap indices, vector indices, learned indices).
- Proficiency in query engine concepts like predicate pushdown and late materialization.
- Familiarity with the storage hierarchy, including cloud object stores, NVMe, block storage, spinning disk, RAM, and GPU memory.
- In-depth knowledge of Parquet, Iceberg, Delta, Lance, and other lakehouse formats.
- A strong passion for databases and query systems.
- Belief that the most efficient read is the one that is elided.
Skills
- Storage systems
- Indexing techniques
- Query optimization
- Database systems
- Open data formats (Parquet, Iceberg, Delta)
- Multimodal data handling
- Rust
- Modern C++
- Vector indices (HNSW, IVF, SCANN)
- Hybrid retrieval systems
- OLAP/lakehouse ecosystem
Location
- San Francisco, CA
Work Type
- In-person
- 4 days/week
Experience Level
- Mid-level
Benefits
- Competitive compensation
- Meaningful startup equity
- Catered lunches and dinners
- Commuter benefit
- Team-building events
- Health, vision, and dental coverage
- Flexible PTO
- Latest Apple equipment
- 401(k) plan with match
About the Company
- Eventual is building a multimodal data warehouse for Physical AI, designed to handle video, sensor, and simulation data efficiently.
- Our open-source engine, Daft, is purpose-built for multimodal AI data and is used at scale by major companies.
- We are backed by significant investment from top venture capital firms and have a world-class team from leading tech companies.
- We are focused on empowering the next generation of Physical AI.