What You'll Achieve- Research, prototype, and ship core parts of the geospatial engine at the heart of Paces - large-scale spatial search and analysis that energy developers depend on.
- Take open-ended, hard problems in computational geometry and performance - over tens of millions of parcels and nationwide infrastructure data - from prototype to production.
- Work across the full spatial stack: DuckDB, Arrow/GeoArrow, file-based spatial indexing, and a OLTP-backed data lake.
- Explore the frontier of geospatial computation - for example geospatial embeddings and foundation models (e.g. Clay) - and decide what's worth bringing into the product.
- Set the bar for correctness and performance on a system where wrong or slow answers carry real cost.
Requirements- 4+ years of experience building data intensive software in a similar role
- Strong computer-science fundamentals: data structures, algorithms, graph theory, and a feel for where time and memory go.
- Fluency in a systems/backend language.
- A track record of building performant, correct backend systems - you can reason about a hot path and read a query plan.
- Experience with DuckDB, Apache Arrow, or comparable columnar/analytical data systems.
- A research mindset: comfort with open-ended problems, prototyping to learn, and knowing when to harden a prototype into a production system.
- Comfort owning ambiguous problems end-to-end and communicating tradeoffs clearly in writing.
About YouYou will thrive in our culture if you:
- Correctness and performance matter to you. You measure before you claim, document your tradeoffs, and prefer a loud failure to a plausible wrong answer.
- You have real depth in the fundamentals and reach for the right primitive, not the familiar one.
- You operate with high agency - you scope ambiguous problems, decide, and own the outcome.
- You're pragmatic: you ship the simplest thing that solves a real problem.
Bonus Points- Experience with a systems language - Rust, C, C++, Zig, or similar.
- A geospatial / GIS background: PostGIS, GDAL/raster drivers, GeoArrow, projections and coordinate systems, or computational-geometry libraries (GEOS, georust/geo).
- ML, embeddings, or geospatial foundation models (e.g. Clay).
- Familiarity with zarr / GeoZarr or other large-scale array/raster formats.
- Large-scale spatial pipelines, spatial indexing, or distributed/ephemeral compute.
- Open source contributor.
Compensation and Benefits- $150K - $220K annual compensation
- Competitive equity compensation
- 401(k) matching
- Health, Dental and Vision insurance
- Paid company holidays & PTO
- Hybrid work in the office in Williamsburg, Brooklyn ~3x per week
