Machine Learning Infrastructure Engineer

WindBorne Systems•Palo Alto, CA

6d•$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. 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.

Requirements

Have experience running production ML systems — you’re not just good at fighting fires but also know how to build systems that don’t catch on fire
Experience with large datasets
Comfortable keeping up with fast-paced model releases and building reliable custom deployments for them
Experience with PyTorch, Docker, cursed memory management, compression and debugging network saturation
Affinity for systems and structure — you can counterbalance a research team’s natural state of chaos with well-organized infrastructure and clear processes

Nice To Haves

Experience with weather data, geospatial pipelines, or scientific computing
Experience with very large datasets, on the petabyte scale
Experience managing GPU clusters or job schedulers

Responsibilities

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, network faults, and storage issues. Build monitoring, auto-recovery, and job scheduling so researchers can launch experiments with less need for babysitting them.