Environmental AI Benchmark Engineer

Crossing Hurdles

Neuseeland

Vor Ort

NZD 90.000 - 130.000

Vollzeit

Vor 2 Tagen
Sei unter den ersten Bewerbenden

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Zusammenfassung

Crossing Hurdles seeks experts to design realistic earth-science tasks that push advanced AI models, with a focus on reproducible environments and robust evaluation criteria. Applicants will develop reference solutions and benchmarks for geospatial analyses, simulations, and forecasting.

The role requires hands-on Python expertise and deep knowledge in earth or environmental science, plus experience benchmarking AI systems on scientific tasks.

Qualifikationen

  • Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
  • Strong practical proficiency with Python.
  • Deep expertise in at least one relevant earth or environmental-science specialization.
  • Hands-on experience using computational methods for geospatial analysis, modeling, forecasting, remote sensing, statistics, or reproducible research.

Aufgaben

  • Create realistic computational earth-science tasks that challenge advanced AI models.
  • Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
  • Evaluate model-generated code, geospatial analyses, simulations, forecasts, and technical deliverables.
  • Identify issues involving coordinate systems, spatial or temporal data, model assumptions, uncertainty, numerical methods, or reproducibility.

Kenntnisse

Python
Geospatial analysis
Reproducible research
Model evaluation

Tools

GeoPandas
NumPy

Jobbeschreibung

Crossing Hurdles seeks experts to design realistic earth-science tasks that push advanced AI models, with a focus on reproducible environments and robust evaluation criteria. Applicants will develop reference solutions and benchmarks for geospatial analyses, simulations, and forecasting.

The role requires hands-on Python expertise and deep knowledge in earth or environmental science, plus experience benchmarking AI systems on scientific tasks.

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