gem
Software Engineer, Geospatial Systems
Paces
Brooklyn, USA
Full-time
Mid Level
Hybrid
USD 150000-220000 yearly
Discovered 1 weeks ago
Free
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- 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.
- 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.
- 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.
- 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.
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