Artificial Intelligence Scientist

Key Responsibilities

• Lead applied AI research to develop novel approaches for agricultural challenges such as crop monitoring, yield forecasting, and sustainability.
• Explore and prototype emerging AI paradigms, including reasoning-enhanced LLMs (e.g., chain-of-thought, self-reflection, tool use), recursive or iterative modeling, reinforcement learning and RLHF-style training, and self-supervised or foundation models.
• Translate research ideas into validated prototypes and production-ready methods.
• Design and evaluate state-of-the-art models across computer vision, NLP, time-series, and multimodal learning (e.g., satellite/drone imagery, sensor data, text).
• Apply modern techniques such as representation learning, domain adaptation, few-shot learning, multimodal fusion, spatiotemporal modeling, and efficient fine-tuning.
• Advance model robustness, generalization, and efficiency under real-world agricultural constraints.
• Integrate domain knowledge from agronomy, climate, and geospatial data into model design and evaluation.
• Develop methods that handle noisy, sparse, seasonal, and region-dependent data, common in agricultural systems.
• Set standards for scientific experimentation, and reproducibility across AI research efforts.
• Mentor engineers and scientists on research methodology, model design, and experimental analysis.
• Collaborate with cross-functional teams and external research partners to align research outcomes with real-world impact.
• Communicate research findings clearly through technical reports, presentations, and internal knowledge sharing.

Relevant Experience