Machine Learning Research Engineer, Model Evaluation

Convectivecapital

Redwood City (CA)

Hybrid

USD 140,000 - 240,000

Full time

14 days+

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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 developing AI weather models that run 24/7 and forecast globally every 20 minutes. We seek a Machine Learning Research Engineer to define robust evaluation strategies, build fast, reproducible analyses, and extend tooling for model comparison across regions and weather regimes.

You will present clear results with uncertainty caveats and collaborate with meteorology and research teams to shape the next generation of forecasts. Hybrid or in‑person work in Redwood City, CA.

Qualifications

Excellent scientific judgment and healthy skepticism.
Strong experimental taste and ability to identify evaluation that answers the question that matters.
Systems thinking to build reusable infrastructure for future work.
Experience evaluating ML systems using geospatial, time‑series, or multi‑dimensional datasets.
Able to investigate ambiguous results, synthesize evidence, and communicate conclusions clearly.
Experience with weather, climate, forecasting, or AI‑assisted research tools is helpful.

Responsibilities

Evaluation strategy — Develop a rigorous, meteorologically valid strategy for comparing WeatherMesh with leading models. Select metrics, datasets, baselines, case studies.
Fast feedback and durable systems — Build quick evaluations that provide useful signals and scale into reusable infrastructure.
Agentic tooling for evals — Improve our evaluation infrastructure, including agentic AI tools for forecasts and results.
Technical communication — Produce clear scorecards, visualizations, and explanations for researchers, leadership, customers, and partners.

Skills

Python

ML tools

Geospatial data

Time-series

Experimental design

Communication

Tools

PyTorch

NumPy

pandas

xarray

Job description

Machine Learning Research Engineer, Model Evaluation

Apply now

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

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