Database Engineer – WherobotsDB

About the role

Wherobots is looking for a passionate, skilled, and experienced Database Engineer to help design, build, and operate WherobotsDB, the distributed query engine that gives analysts and AI agents context about the physical world.

Physical-world data is multimodal: building footprints and road networks, satellite and aerial imagery, weather and climate data cubes, model embeddings, GPS traces and sensor time series, and the business tables they connect to. Each modality has its own formats, access patterns, and failure modes at scale. This is a distributed query engine role. You will spend most of your time inside the engine, building the query planning, partitioning, shuffle, operators, and Rust-native execution that process all of these modalities together over billions of records across a cluster. If you can reason about a query plan from first principles, find the bottleneck in a distributed join, and turn a research idea into a production operator, we should talk.

We are 100% cloud-native and build our product using modern, reliable tooling. WherobotsDB is a fully distributed query engine with a Rust-native execution layer. It plans and runs each query across a cluster of machines, processes vector and raster data in the same engine, and joins both with tabular data in SQL. Data lives on object storage in open formats such as Apache Iceberg, Parquet, Cloud-Optimized GeoTIFF (COG), and Zarr.

If you are passionate about building a distributed query engine for physical-world data and want to be part of a fast-growing company at the forefront of AI for the physical world, we would love to hear from you. Apply now and join the Wherobots team!

Responsibilities

• Build the distributed query engine: Design how WherobotsDB plans, partitions, shuffles, and executes queries across a cluster, from the logical plan down to the physical operators on each worker.
• Design and build distributed operators: Implement join, nearest-neighbor, and aggregation operators that scale across a cluster and work on geometries, imagery, tensors, and tabular data, with careful attention to correctness on messy real-world inputs.
• Own partitioning and pruning: Design partitioning, indexing, and pruning strategies so skewed datasets, from GPS traces to imagery archives and data cubes, stay balanced across workers and each query reads only the files and chunks it needs.
• Bring data modalities together: Extend how WherobotsDB stores, reads, and processes vector, raster, tensor, and tabular data, from Zarr data cubes to model embeddings, so users can join them with business records in a single query.
• Extend the native execution layer: Build high-performance operators in Rust and integrate them with query planning and memory management.
• Build on open table and file formats: Use the statistics and metadata in Iceberg, Parquet, COG, and Zarr to push filters down to object storage.
• Measure and improve performance: Use benchmarks and customer workloads to find bottlenecks across memory, I/O, network, shuffle, and serialization, then ship the fixes.
• Support production workloads: Diagnose memory, skew, and correctness issues in customer jobs, and turn each root cause into an engine fix or a documented limit.
• Contribute to open source: Make contributions to Apache Sedona and other open-source projects across the data ecosystem, such as Apache DataFusion, Apache Arrow, Apache Iceberg, and Zarr.
• Raise the bar: Provide technical leadership on architecture, engineering standards, and roadmap. Write design docs before major changes, review code across the team, and hold every change to the same quality bar, including code written with AI coding assistants.

Qualifications

• 5+ years of experience building distributed query engines or databases, or a PhD in computer science or a related field with research in databases or distributed systems.
• Deep understanding of distributed query processing: planning and optimization, distributed joins, shuffle and data exchange, partitioning, skew handling, and memory management.
• Strong proficiency in Rust (or C/C++) and Python.
• Experience with columnar execution engines and formats such as Apache Arrow, Apache DataFusion, Parquet, or Iceberg.
• Experience processing data beyond flat tables, such as geometries, imagery, N-dimensional arrays and tensors, time series, or nested and semi-structured data.
• A track record of high-quality, maintainable, and observable software, and clear technical writing.
• Strong collaboration skills in a hybrid or distributed team.

Nice to Have (Optional)

• Papers published at top database conferences, such as ACM SIGMOD, VLDB, IEEE ICDE, or ACM SIGSPATIAL.
• Committer or significant contributor to Apache DataFusion, Arrow, Iceberg, Parquet, Sedona, or DuckDB.
• Experience with Apache Spark internals, such as Catalyst rules or physical planning.
• Experience with distributed scheduling, fault tolerance, or elastic scaling on cloud infrastructure.
• Experience with geospatial data, such as spatial indexes (R-tree, S2, or H3), spatial joins, or coordinate reference systems.
• Experience processing imagery or N-dimensional array data at scale, such as COG or Zarr.
• Proficiency in Scala and Java.

Compensation and benefits

Wherobots offers competitive compensation, equity, and benefits. The base salary range for this position is $185k-$275k per year.

Preferred location: Seattle. Candidates must currently live in the United States, with work authorization that allows them to join immediately. We provide flexibility in working arrangements for most roles, including remote, hybrid, and in-office options. For candidates who receive an offer, base pay varies based on location, seniority, skills, and experience.

Our benefits package for full-time employees includes:

• 100% coverage of medical, dental, and vision insurance.
• Access to a 401(k) retirement plan.
• Unlimited paid time off.

About Wherobots

Wherobots is the AI Context Engine for the Physical World: the missing infrastructure layer for AI that needs to reason about our physical reality. Existing AI context infrastructure was built for text, code, documents, databases, and the internet. The most consequential enterprise decisions also depend on the physical world: supply chains, climate exposure, geopolitics, infrastructure, and more.

Our two products let teams use familiar SQL and Python to analyze physical-world data and give AI agents persistent context about their organization's assets and operations:

• WherobotsDB is a fully distributed spatial analytics engine with a Rust-native execution layer. It processes vector and raster data in the same engine at scale, enabling precise spatial joins across billions of geometries and analysis that connects imagery with business data.
• RasterFlow is a fully distributed, serverless engine for raster preparation and computer vision. It processes satellite imagery at planetary scale, runs built-in or custom PyTorch models, and turns imagery into predictions and spatial features that teams can analyze with WherobotsDB.

Wherobots was founded by the original creators of Apache Sedona, the open-source spatial computing platform with more than 80 million downloads. Apache Sedona is used in production by tens of thousands of organizations, including many Fortune 500 companies.