Machine Learning Research Engineer, Model Evaluation

Responsibilities

• Work with our Meteorology team to develop a rigorous, meteorologically valid strategy for comparing WeatherMesh with leading AI and physics-based models.
• Choose the metrics, datasets, baselines, and case studies that provide an honest picture of forecast quality.
• Build quick evaluations that give researchers useful signals, then turn recurring analyses into reliable, reusable infrastructure.
• Think systematically about reproducibility, provenance, and how evaluation tools fit into the broader research workflow.
• Improve our existing evaluation infrastructure, including agentic AI-based tools for investigating forecasts and synthesizing results.
• Produce clear scorecards, visualizations, and explanations for researchers, leadership, customers, and external partners.
• Communicate model performance precisely, including uncertainty and important caveats.

Qualifications

• Excellent scientific judgment and healthy skepticism.
• You ask whether a comparison is fair, what else could explain a result, and what evidence would change your mind.
• Strong experimental taste: you can identify the evaluation that answers the question that matters and distinguish robust improvement from noise.
• Systems thinking: you can solve today’s problem while recognizing what should become reusable infrastructure for future work.
• Experience evaluating ML systems using large, scientific, geospatial, multidimensional, or time-series datasets.
• Strong Python skills and experience with scientific and ML tools such as PyTorch, NumPy, pandas, or xarray.
• Able to investigate ambiguous results independently, synthesize evidence, and communicate conclusions clearly.
• Experience with weather, climate, forecasting, physical science, or AI-assisted research tools is helpful, but not required.

Benefits

• 401(k)
• Dental, health, and vision insurance
• Unlimited PTO
• Stock Option Plan
• Office food and beverages

Pay

$140k–$240k. We consider a range of backgrounds and experience levels and adjust offers to be competitive with market rates.

Schedule

Hybrid or in-person.