Machine Learning Research Engineer, Model Evaluation

WindBorne Systems•Palo Alto, CA

1d•$140,000 - $240,000•Hybrid

About The Position

WindBorne Systems is supercharging weather forecasts with a proprietary data source: a global constellation of next-generation smart weather balloons targeting critical atmospheric data. We design, manufacture, and operate our own balloons, using their observations to generate otherwise unattainable weather intelligence. Our mission is to eliminate weather uncertainty and help humanity adapt to climate change—whether by predicting hurricanes or speeding the adoption of renewables. The founding team of Stanford engineers was named Forbes 2019 30 Under 30 and is backed by top-tier investors, including Khosla Ventures and Footwork VC. WindBorne builds AI weather models that run 24/7, producing global forecasts every 20 minutes. Evaluating these models is much harder than producing a headline accuracy number: performance varies across regions, lead times, weather regimes, and customer use cases, while standard metrics often fail to capture what makes a forecast meteorologically sound or useful. We need someone with excellent scientific taste to determine where our models excel, where they fail, and which results we should trust. You will work at the intersection of machine learning and meteorology, combining fast analyses with robust systems that make future research faster and more reliable.

Requirements

Excellent scientific judgment and healthy skepticism.
Strong experimental taste to identify the evaluation that answers the question that matters and distinguish robust improvement from noise.
Systems thinking to 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.
Ability to investigate ambiguous results independently, synthesize evidence, and communicate conclusions clearly.

Nice To Haves

Experience with weather, climate, forecasting, physical science, or AI-assisted research tools.

Responsibilities

Develop a rigorous, meteorologically valid strategy for comparing WeatherMesh with leading AI and physics-based models, including choosing metrics, datasets, baselines, and case studies.
Build quick evaluations that give researchers useful signals, and turn recurring analyses into reliable, reusable infrastructure, focusing on reproducibility and provenance.
Improve 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, communicating model performance precisely, including uncertainty and important caveats.