Senior AI Scientist (SF)

Requirements: PhD (or PhD-equivalent research record) in Computer Science, Electrical Engineering, or a closely related field completed within the last ~3 years. 3 + years leading novel research as a post-doc or at a deep-tech startup. First-author publications in premier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or TPAMI—ideally on structured prediction, probabilistic modelling, or large-scale visual recognition. Demonstrated ability to translate novel algorithms into working code (e.g., open-sourced research prototype or competition-winning implementation). Fluency in modern ML tooling—Python, PyTorch or JAX, version-controlled experiments, GPU/TPU training workflows. Strong grounding in statistical learning theory, graphical models, and Deep Learning based models; Excellent written and oral communication—able to explain a structured-prediction model to engineers and geologists alike. Core Skills: what you'll use every week Domain What "great" looks like Structured & Probabilistic ML Factor-graph / CRF / structured SVM intuition; comfortable extending to 3-D geospatial grids and physics constraints. Hybrid AI × Physics thinking Knows when to enforce conservation laws in the loss and when to let the network learn. Experimental discipline Designs ablation studies, uncertainty calibration, and significance testing. Hands-on Hardware Integration & Robotics Rapid-prototypes sensor interfaces, syncs ML pipelines with on-device firmware, and loves a lab full of oscilloscopes. Collaboration & mentorship Leads reading groups, reviews PRs for research quality, and coaches junior scientists. Good-to-Have Prior work on remote-sensing, climate, or geoscience data (e.g., hyperspectral cubes, DEMs, gravity or magnetics). Hands-on with graph neural nets, diffusion models, or PINNs for physical systems. Experience with robotics teams, hackathons, or organizing niche maker events Patents or industry internships showing an ability to ship research to production