Machine Learning Research Engineer, Model Evaluation

WindBorne Systems; Palo Alto (CA); On-site; USD 140,000 - 240,000; Full time

Benefits offered by this job

401(k)

Dental, health, and vision insurance

Unlimited PTO

Stock Option Plan

Office food and beverages

Job summary

WindBorne Systems is seeking a Machine Learning Research Engineer focused on Model Evaluation to advance our AI weather forecast platform. You will design robust evaluation strategies, build fast, reproducible analyses, and communicate findings clearly to researchers, leaders, and customers.

You will work at the intersection of machine learning and meteorology, ensuring our WeatherMesh evaluations capture regional variations, lead times, and real-world use cases.

Qualifications

• + Excellent scientific judgment and healthy skepticism about evaluations.
• + Strong experimental taste to identify meaningful evaluation results.
• + Systems thinking to build reusable evaluation infrastructure.
• + Experience with weather, climate, forecasting, AI-assisted research is a plus.

Responsibilities

• + Evaluation strategy — Develop a rigorous, meteorologically valid strategy for comparing WeatherMesh with leading models.
• + Fast feedback and durable systems — Build quick evaluations and reusable infrastructure with reproducibility in mind.
• + Agentic tooling for evals — Improve evaluation infrastructure including AI-based tooling for investigations.
• + Technical communication — Create scorecards, visualizations, and clear explanations for researchers and leadership.

Skills

Python

Experiment design

Geospatial analysis

Time-series analysis

Systems thinking

Scientific skepticism

Communication

Research methods

Python debugging

Tools

PyTorch

NumPy

pandas

xarray

Job description

Machine Learning Research Engineer, Model Evaluation

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.

Responsibilities

• Evaluation strategy — 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.


• Fast feedback and durable systems — 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.


• Agentic tooling for evals — Improve our existing evaluation infrastructure, including agentic AI-based tools for investigating forecasts and synthesizing results.


• Technical communication — Produce clear scorecards, visualizations, and explanations for researchers, leadership, customers, and external partners. Communicate model performance precisely, including uncertainty and important caveats.

Skills and Qualifications

Requirements

• 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

Salary

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

Location

1600 Bridge Pkwy, Redwood City, CA. Hybrid or in-person.