Research Scientist, AnthroKrishi, DeepMind

Minimum qualifications:

• PhD or equivalent practical research experience in Computer Science, AI, or a related field with a focus on computer vision or machine learning.
• 2 years of experience in building computer vision models using machine learning techniques.
• One or more scientific publications in the ML/AI conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR).
  

Preferred qualifications:

• Experience mentoring students or junior researchers.
  
• Experience building and training deep learning models in frameworks such as JAX, TensorFlow, or PyTorch.
• Experience with or a strong interest in remote sensing or geospatial data.
  
• Demonstrated expertise in one or more of the following: generative models, segmentation algorithms, multi-modal fusion, spatio-temporal analysis.
  
• A passion for applying AI to solve large-scale societal challenges like climate change and food security.
  

About the job

The AnthroKrishi team at DeepMind is building a digital understanding of global agriculture to ensure a sustainable and food-secure future. We develop and deploy AI models that operate at a planetary scale, turning satellite data into actionable intelligence. Our work helps farmers, policymakers, and humanitarian organizations worldwide. We are solving fundamental AI challenges to address one of humanity's greatest needs.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities

• Pioneer novel computer vision models to create a unified understanding of agriculture from satellite data sources.
  
• Solve core AI problems by developing generalizable models that are robust across varied agricultural systems, from smallholder farms to large-scale industrial fields.
  
• Lead research toward the grand challenge of field-level crop yield forecasting. This involves advancing spatio-temporal reasoning and learning effectively from limited or sparse data.
  
• Design and execute large-scale experiments, writing high-quality, reusable code (JAX preferred), and contributing to a production-ready system.
  
• Mentor junior researchers, collaborating with cross-functional teams across Google, and publishing your work at conferences.