- – Design and implement machine learning models for spatial and temporal datasets.
- – Build and optimize data pipelines for ingesting and preparing spatial data.
- – Deploy real-time inference services for delivering spatial insights at scale.
- – Design and own distributed architecture for DGGS as a referencing layer.
- – Drive end-to-end system design decisions with full accountability for tradeoffs.
- – Build spatial indexing infrastructure for real-time, cross-scale data access.
- – Design and implement production-grade geospatial libraries with clean APIs.
- – Build high-performance C++ core systems with Python bindings for accessibility.
- – Develop and optimize computational geometry algorithms for large-scale analysis.