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AI Water Quality Prediction Engineer

Chloe Schneider

Full-time · Job · New_York

About the role

Waterborne disease outbreaks are almost always detectable before they become health crises — if you're monitoring the right signals. Turbidity spikes, chlorine residual drops, pathogen DNA traces in wastewater, temperature anomalies in distribution networks: these signals precede incidents by hours to days. We build the ML systems that help municipal water utilities and environmental regulators detect those signals before people get sick. Our platform monitors 340 water systems across North America and the EU, processing sensor telemetry, lab test results, and satellite-derived watershed data in near-real-time. The prediction challenge is multi-target: contaminant type matters as much as contamination presence, and the tolerance for false negatives (missed real threats) is much lower than for false positives. You'll own the prediction model stack: redesigning from single-sensor threshold models to multi-modal fusion that combines sensor arrays, historical patterns, and upstream watershed events. You'll also build the calibration framework that lets each utility configure the sensitivity trade-off appropriate for their risk tolerance and regulatory context.

Responsibilities

  • Redesign water quality prediction models from threshold-based to multi-modal ML
  • Build fusion models combining sensor arrays, lab results, and watershed remote sensing
  • Develop per-utility sensitivity calibration reflecting regulatory risk tolerances
  • Implement real-time prediction serving with sub-60-second latency from sensor event
  • Create evaluation frameworks calibrated to public health impact, not just accuracy metrics
  • Collaborate with water safety domain experts and municipal utility partners

Requirements

  • 6+ years in environmental data science, IoT ML, or public health analytics
  • Experience with multi-sensor fusion and time series prediction under sparse labels
  • Strong Python, PyTorch, and distributed computing skills
  • Understanding of water quality monitoring frameworks and regulatory standards
  • Ability to collaborate with non-technical domain experts and government clients

Benefits

  • Fully remote with flexible schedule
  • Comprehensive health benefits
  • $4,000 annual professional development budget
  • Meaningful public health impact
  • Equity participation

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Categories

data science & analytics AI & Machine Learning Engineers

Skills

python pytorch spark sql gcp

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