Artificial Intelligence Scientist

Role Overview 

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 

• 4+ years of experience in AI/ML model design, training, and deployment in production environments.  
• Proven expertise in building and optimizing models, including LLMs, VLMs, computer vision, and multimodal architecture. 
• Experience with modern learning paradigms such as transfer learning, self-supervised learning, domain generalization, and few-shot or representation learning. 
• Experience with emerging and novel techniques, including retrieval-augmented generation (RAG), diffusion models, reasoning-enhanced LLMs (e.g., chain-of-thought, self-reflection), and reinforcement learning–based training or optimization. 
• Strong programming skills in Python with solid knowledge of data structures, algorithms, and software engineering best practices.  
• Hands-on experience with large-scale data sets, data lake architectures and distributed data processing  
• Fluency in ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and MLOps practices (CI/CD, experiment tracking, reproducibility).  
• Strong technical communication skills, with the ability to document research, present results, and collaborate effectively across technical and non-technical teams. 
• Proven ability to stay current with AI research, critically evaluate new methods, and apply them to complex real-world problems. 

Academic Requirements 

• PhD or master's in computer science, computer engineering, statistics, or mathematics 
• Strong publication record in reputable conferences or journals in AI, machine learning, computer vision, NLP, or related areas