AI Research Scientist

• Ph.D is mandatory
  
• Good experience in publications
  
• Excellent communication
  
• Work Location-Bengaluru
  
• Mode of Work: Hybrid
  

Role Summary

The AI Research Scientist will lead the development of novel machine learning models, algorithms, and foundational AI technologies that advance the organization’s strategic capabilities. This role combines deep research expertise with hands-on experimentation, prototyping, and production quality rigor.

Key Responsibilities:

Research & Innovation

• Conduct original research in machine learning, deep learning, generative AI, reinforcement learning, optimization, or related fields.
  

• Investigate emerging AI methodologies (e.g., multi-agent systems, self-supervised learning, neuro-symbolic reasoning, causal AI).
  

Model Development & Experimentation

• Build, train, and evaluate advanced ML models using modern deep learning frameworks (PyTorch, JAX, TensorFlow).
  

• Design experiments, conduct ablation studies, and run reproducible research pipelines.
  

Data & Infrastructure

• Design and refine large-scale training pipelines, data generation/augmentation strategies, and synthetic data methods.
  

• Utilize distributed training systems, GPU/TPU-based infrastructure, and model optimization frameworks.
  

• Apply responsible AI principles, bias mitigation techniques, and model evaluation methodology aligned to organizational standards.
  

Required Qualifications

• Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, or related field; or equivalent research experience.
  

• Strong publication record in top-tier ML/AI conferences or demonstrable contributions to open-source AI research.
  

• Deep expertise in at least one major area:
  

• Deep learning
  

• Natural language processing
  

• Computer vision
  

• Reinforcement learning
  

• Optimization
  

• Multi-agent systems
  

• Proficiency in modern ML frameworks (PyTorch, TensorFlow, JAX).
  

• Strong mathematics foundation (linear algebra, probability, statistics, optimization).
  

• Experience with large-scale training, experimentation, and distributed computing.