AI Researcher

Job Summary

Required Qualifications

• •Minimum 3 peer-reviewed research publications in Q1 journals and/or A conferences, through any qualifying combination.
• •The publications must represent substantive research contributions in Artificial Intelligence, Machine Learning, Deep Learning, Computer Science, or a closely related field.
• •Candidates will be evaluated primarily on the quality, relevance, originality, and depth of their research contributions.

Desired Qualifications

• •Generative AI
• •Large Language Models
• •Computer Vision
• •Multimodal AI
• •Natural Language Processing
• •Reinforcement Learning
• •Representation Learning
• •Recommendation Systems
• •Search and Information Retrieval
• •Generative Models
• •AI Agents and Autonomous Systems
• •Speech and Audio AI
• •Time-Series Modeling and Forecasting
• •Graph Machine Learning
• •Robotics and Embodied AI
• •Optimization and Learning Algorithms
• •Other emerging AI/ML research areas
• •Deep expertise in one specialization is preferred over superficial knowledge across multiple areas.
• •No candidate is expected to be an expert in all of the above domains.
• •Research and experiment with foundation models and modern neural architectures.
• •Explore LLMs, transformer architectures, multimodal models, and generative models.
• •Investigate prompting, fine-tuning, instruction tuning, model adaptation, distillation, and efficient inference.
• •Research Retrieval-Augmented Generation, semantic retrieval, and knowledge systems.
• •Explore AI agents, tool use, planning, memory, reasoning, and multi-agent architectures.
• •Evaluate open-source and proprietary models for specific applications.
• •Develop techniques to improve factuality, reasoning, robustness, and reliability.
• •Research computer vision and visual representation learning techniques.
• •Explore image understanding, detection, segmentation, classification, generation, and visual reasoning.
• •Research vision-language and multimodal foundation models.
• •Develop multimodal retrieval, reasoning, and generation systems.
• •Explore image, video, and cross-modal understanding.
• •Build datasets and experimental pipelines for model training, fine-tuning, and evaluation.
• •Design appropriate benchmarks, metrics, and evaluation methodologies for your research domain.
• •Perform quantitative and qualitative analysis of model performance.
• •Conduct error analysis and investigate model failure modes.
• •Perform ablation studies and controlled experiments.
• •Compare approaches across relevant quality, computational, latency, scalability, and efficiency metrics.
• •Develop reproducible research and evaluation pipelines.
• •Translate research concepts into practical AI capabilities and prototypes.
• •Research intelligent systems for problems such as search, recommendation, prediction, classification, reasoning, optimization, perception, and decision-making.
• •Develop domain-specific AI systems.
• •Identify promising academic research and evaluate its applicability to real-world problems.
• •Collaborate with AI Engineering, Software Engineering, Product, and Platform teams to transition successful research prototypes into production.
• •Investigate AI robustness, reliability, interpretability, and explainability.
• •Evaluate models for failure modes, hallucinations, bias, adversarial behavior, and other risks.
• •Develop appropriate evaluation methodologies and guardrails.
• •Research methods for improving model transparency and reliability.
• •Contribute to responsible AI research practices.
• •Identify novel and impactful research problems.
• •Develop research hypotheses and novel approaches.
• •Publish research in high-quality journals and conferences.
• •Present research findings to internal and external technical audiences.
• •Track developments from leading AI research labs, conferences, journals, and open-source communities.
• •Contribute to research publications, patents, technical reports, and open-source projects where applicable.
• •Research Problem → Hypothesis → Novel Approach → Implementation → Rigorous Experimentation → Evaluation → Publication → Practical Impact
• •The successful candidate does not need to be an expert in every area of AI.
• •We are looking for strong researchers with deep expertise in a particular AI/ML domain who can contribute to a broader AI research organization, collaborate across disciplines, and learn adjacent technologies when required.