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# Founding Geospatial Machine Learning Engineer

- **Company:** Homex Corporation
- **Location:** New York, NY, United States
- **Salary:** $310,000
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Information Engineering, Distributed Computing Environment, Spatial Databases, Python (Programming Language), Machine Learning, PostGIS, Scientific Computating, Pytorch, Model Validation, Geospatial Data Abstraction Library (GDAL), Pure Data, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25196_e00ff6a8489d97ce306fd9e7787cb655

## About the Role

* Six or more years in applied machine learning, scientific computing, geospatial modeling, forecasting, or a related field, with staff-level ownership or equivalent evidence.
 * Strong Python and modern ML framework experience, including production model development.
 * Skill with probabilistic or time-series evaluation, uncertainty, calibration, or comparable statistical rigor.
 * Experience with geospatial data, coordinate systems, spatial joins, geographic hierarchies, and large spatial datasets.
 * Experience building reproducible training and evaluation systems rather than notebook-only analysis.
 * Ability to move between research questions, data engineering, model implementation, and production constraints.
 * Clear scientific writing and the judgment to state limitations precisely.

Helpful, not required

 * Public records, land use, transportation, infrastructure, utilities, climate, demography, or economic forecasting.
 * PyTorch, distributed training, spatial databases, GeoPandas, xarray, rasterio, GDAL, PostGIS, or equivalent systems.
 * Work with planners, government analysts, regulated industries, or independent technical reviewers., This is not a pure data-engineering position, remote-sensing-only position, or academic research appointment. You must improve model capability, evaluation credibility, and production delivery together.

## Description

* Design, train, evaluate, and deploy forecasting models across parcels, buildings, neighborhoods, infrastructure, utilities, and regional indicators.
 * Build leakage-resistant historical datasets with explicit vintages, geographic crosswalks, target definitions, and reproducible feature construction.
 * Establish benchmarks across places, horizons, baselines, and public planning models.
 * Measure point accuracy, probabilistic scores, calibration, coverage, tails, geographic transfer, and failure modes.
 * Build uncertainty estimates and explanations that are technically defensible and useful to practitioners.
 * Develop and test intervention-conditioned or scenario models without overstating causal identification.
 * Own experiment tracking, model lineage, data quality checks, training reproducibility, and model cards.
 * Partner with the product engineer to deploy models through stable services with monitoring, cost controls, and rollback capability.
 * Work with public-sector practitioners and independent reviewers to turn domain criticism into better datasets, tests, and model behavior.
 * Communicate methods and limitations clearly in technical documents, customer materials, and diligence artifacts., * Reproduce the current principal benchmark from source data through published metrics.
 * Audit target definitions, vintages, leakage controls, geographic joins, and baseline comparability.
 * Define the model evaluation contract for one-year and multi-year horizons.
 * Produce a prioritized research and engineering plan tied to the first paid evaluation.
 * Ship one material improvement to model performance, calibration, geographic coverage, or evaluation reliability.

6 TO 12 MONTHS

 * A reproducible multi-jurisdiction benchmark supports customer and investor diligence.
 * Forecast and uncertainty metrics are monitored by geography, horizon, cohort, and target.
 * New data sources can be added through documented, tested spatial and temporal contracts.
 * Models move from experiment to production through a controlled and observable release process.
 * The first paid evaluations have independent technical review and defensible acceptance evidence.