AI/ML Engineer

3+ years in applied ML engineering. Developed or contributed to at least one end-to-end deep-learning system. Strong software-engineering skills (Python, Linux, GitHub Actions, Docker). Comfort with distributed training on GPUs. Familiarity with scientific data formats (NetCDF, Zarr) and geospatial arrays. Ability to explain complex ML choices to domain scientists. Experience with physics-informed ML, operator learning, or uncertainty-aware surrogate modeling (preferred). Prior work in climate, weather, or remote-sensing ML (preferred). Ability to communicate with impact and confidence (preferred). Ability to work in person in the SF Bay Area 2-3 days per week (preferred).

Design, implement, and benchmark surrogate architectures for atmospheric and aerosol processes. Build reproducible data pre-processing and training pipelines. Design and implement state-of-the-art computer vision models, including Vision Transformers and generative approaches. Automate hyper-parameter sweeps and uncertainty calibration on multi-GPU clusters. Containerize and document workflows for model retraining/fine-tuning. Collaborate on releases, tutorials, and user-support channels.