Machine Learning Engineer

Overview:
We are looking for a Senior ML Engineer with extensive experience in neural networks (NNs), LSTM architectures, and prescriptive modeling. A background in hydrology or environmental modeling is a strong plus. The engineer will develop and optimize simulation and decision-support models to forecast and mitigate floods, droughts, and compound extreme events.

Key Responsibilities:
• Predictive Modeling: Design and train neural network models (LSTM, RNN, CNN) for hydrological forecasting and time-series analysis of basin or climate data
• Prescriptive Modeling & Simulation: Develop simulation and optimization models using mass balance equations and hydrological process representations
• Scenario Analysis: Build models to predict floods, droughts, and extreme events; benchmark simulations against historical and observed datasets
• Optimization & Decision Support: Apply simulation/optimization libraries such as Pyomo, Gurobi, SimPy, and metaheuristic approaches for decision-making under uncertainty
• Geospatial Integration: Integrate models with geospatial and climate data sources to enable real-time scenario simulations
• Explainability & Uncertainty: Embed explainability and uncertainty quantification layers into model outputs to enhance stakeholder trust and interpretability
• Transparency & Documentation: Ensure comprehensive model documentation, benchmarking procedures, and reproducibility for scientific and stakeholder validation

Required Qualifications:
• Strong background in Neural Networks (NNs), LSTM, and time-series modeling
• Proven experience with simulation and optimization frameworks (Pyomo, Gurobi, SimPy, or metaheuristics)
• Knowledge of mass balance modeling, hydrological processes, and climate data systems
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Familiarity with geospatial data integration (GIS, raster/vector datasets)
• Experience implementing model explainability and uncertainty assessment
• Excellent analytical and documentation skills for model transparency and validation

Nice to have:
• Background in hydrology, water resources, or climate modeling
• Experience with prescriptive analytics and decision-support systems for environmental domains