Machine Learning Infrastructure Engineer
Position Summary & Responsibilities
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. Our research team is small and moves fast, but too much of their time goes to operationalization and infra firefighting instead of model development. We need someone to fix that.
Responsibilities
What you’d own:
Research to Operations pipelines — Our models serve real-time forecasts to customers with strict latency requirements. You'd own uptime end-to-end: build health monitoring, improve logging, diagnose failures across nodes.
Inference scaling & compute strategy — We have an on-prem cluster but also use cloud providers, especially for production deployments. You'd evaluate cost/performance tradeoffs across cloud options as we scale, and also help manage growing on-prem resources for compute and storage.
Data pipelines & upstream reliability — Weather data comes from dozens of sources (satellites, government agencies, our own balloon observations) with varying schedules, incomplete documentation and sometimes failing or changing quality. You'd build pipelines for training and realtime data that gracefully handle upstream delays, do QC checks on data, and add logging and alerting for a zoo of edge cases.
Training infrastructure — Make distributed training runs reliable. They die from silent OOMs, net
Key Role Competencies & Evaluation Highlights
Evaluating hard-core hardware/software integration, atmospheric science intuition, and extreme engineering resilience for global weather data collection.
Apply for Machine Learning Infrastructure Engineer
Setup your contact terms & referral note to get recommended directly to the hiring lead at Windborne Systems.