About the role
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. Weather forecasting presents unusually rich machine learning problems: enormous, messy datasets; imperfect ground truth; complex physical structure; and models that must work reliably in the real world. We are looking for a broadly capable ML researcher and engineer who is passionate about machine learning and excited to push our models forward. We care more about your ability to identify important problems and make progress on them than whether your previous work fits a particular specialty.
Responsibilities What you’d own: AI-based data assimilation — Develop methods for incorporating real-time observations from balloons, satellites, weather stations, and other sources into our forecasts. A foundation model for weather — Work toward a single model capable of predicting many weather-related datasets, including variables and data products not covered by traditional global forecasts. Messy, large weather datasets — Find, understand, clean, and combine large datasets with inconsistent formats, resolutions, coverage, and quality. Determine which data is actually useful and build systems that make it easier to use again. Rapid experiments — Test ideas quickly, learn from failures, and follow promising results into the weeds and chase down another 1% of model improvement. Infrastructure and systems — Turn successful experiments into reusable systems that accelerate future research. Research direction — Form hypotheses, design convincing experiments, keep up with relevant ML research, and help decide which ideas are worth pursuing. Whatever the problem needs — Venture into operations, infrastructure, evaluation, data engineering, or other technical side quests when needed to get the research working in practice. Skills and
