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Strong Tier-1 brand and metro location increase competition, though PhD research specialization narrows applicant pool.
Requires PhD-level ML/vision research expertise, limiting cross-industry transferability.
PhD, publications, and specific ML research skills make hiring filters strict.
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Develop novel computer vision models utilizing satellite data to generate a comprehensive understanding of global agriculture.
Lead research initiatives focusing on field-level crop yield forecasting using spatio-temporal reasoning and learning from limited data.
Design and execute large-scale experiments with high-quality, reusable code (preferably JAX), contributing to production-ready AI systems impacting food security and climate change.
PhD or equivalent practical research experience in Computer Science, AI, or related fields focusing on computer vision or machine learning.
At least 2 years of experience building computer vision models using machine learning techniques.
One or more scientific publications in reputable ML/AI conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR).
Work Experience Required: Minimum 2 years in computer vision model building using ML.
Experience mentoring students or junior researchers demonstrating leadership in research settings.
Proficient in deep learning frameworks such as JAX, TensorFlow, or PyTorch with preference for JAX.
Background or strong interest in remote sensing, geospatial data, and expertise in generative models, segmentation algorithms, multi-modal fusion, or spatio-temporal analysis aligned with solving large-scale societal challenges.