Geospatial ML Engineer

Distributed Spectrum•New York City, NY

2d•Onsite

About The Position

We’re Hiring a Geospatial Machine Learning Engineer to help pioneer our newest foundational model to enable us to not only detect, but track physical devices transmitting in the RF spectrum You’ll shape how we identify the right data and ingest it, how we train and test, and the tooling we develop to iterate on the model. You will also serve as the organization’s geospatial machine learning expert, partnering with the founders and the growth team to understand customer needs, and translating them into capabilities and products for impact.

Requirements

Deep GIS & geospatial ML expertise shipping models that reason over maps, satellite imagery, or navigation data.
Comfort with building for rigorous domain adaptation, ensuring performance even in unknown domains, as often customers cannot tell us where they will deploy our technology.
Fluency with coordinate systems & map projections
Experience in autonomous systems, remote sensing, or visual navigation
End-to-end model deployment to embedded/edge runtimes (CUDA, ONNX, TensorRT, etc.).
Core Stack: Python, PyTorch, Linux

Nice To Haves

C++/Rust for performance, drone-based navigation, probabilistic filtering, topo-aware path planning.

Responsibilities

Own the geospatial ML pipeline and collect, clean, and model map-centric data to feed localization and propagation algorithms.
Build our foundational, deployable geospatial model that can run on size/weight-constrained drone payloads and forward devices worldwide, and go from ideation to robust, reliable model in 12 months.
Define best-in-class geo data practices—CRS choices, tiling strategies, temporal alignment, and robust validation.
Take ideas from whiteboard to field demos quickly