Founding Geospatial Machine Learning Engineer

Founding Geospatial Machine Learning Engineer Turn our physical development model into rigorously validated, production-ready spatial forecasting and scenario capability. The roadmap spans probabilistic forecasting, geographic transfer, historical-vintage controls, cross-jurisdiction benchmarks, calibration, explainability, multi-target modeling, and intervention-conditioned scenarios. That scientific and engineering responsibility should not remain indefinitely concentrated in the founder. What you will own 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. FIRST 90 DAYSEstablish the foundation 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 MONTHSA 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.