AI Research Scientist

Job Title

AI Research Scientist

Location

Hybrid / Remote

Employment Type

Full-time

Job Summary

We are seeking an AI Research Scientist to conduct cutting-edge research in artificial intelligence, machine learning, and generative AI. The ideal candidate will develop Client algorithms, improve existing AI models, publish research findings, and translate research into scalable AI solutions. This role involves close collaboration with AI engineers, data scientists, product teams, and academic or industry partners to advance the organization's AI capabilities.

Key Responsibilities

• Conduct research in machine learning, deep learning, natural language processing (NLP), computer vision, reinforcement learning, or generative AI.
• Design, develop, and evaluate Client AI models, algorithms, and architectures.
• Research and optimize Large Language Models (LLMs), multimodal AI systems, and Retrieval-Augmented Generation (RAG) techniques.
• Develop proof-of-concept (PoC) solutions to validate research ideas.
• Analyze large-scale datasets and perform statistical modeling and experimentation.
• Design and execute experiments to evaluate model performance, scalability, robustness, and efficiency.
• Publish research papers, technical reports, patents, or white papers where applicable.
• Stay current with emerging AI research, industry trends, and open-source technologies.
• Collaborate with engineering teams to transition research into production-ready AI solutions.
• Present research findings to technical and non-technical stakeholders.
• Ensure AI solutions align with Responsible AI, ethics, fairness, privacy, and security principles.
• Mentor junior researchers and contribute to the organization's AI research strategy.

Required Qualifications

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, Mathematics, Electrical Engineering, or a related field.
• 3–8+ years of research or industry experience in AI and machine learning (or equivalent research experience for recent Ph.D. graduates).
• Strong foundation in mathematics, statistics, probability, and optimization.
• Experience developing and evaluating machine learning and deep learning models.
• Proficiency in Python and scientific computing libraries.

Preferred Qualifications

• Ph.D. in Artificial Intelligence, Machine Learning, or a closely related discipline.
• Publications in leading AI conferences or journals (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV).
• Experience with foundation models, LLMs, multimodal AI, or AI agents.
• Knowledge of Responsible AI, AI safety, and model evaluation methodologies.
• Experience working in cloud or distributed computing environments.

Technical Skills

• Python
• PyTorch and/or TensorFlow
• NumPy, Pandas, SciPy
• Machine learning algorithms
• Deep learning architectures
• Natural Language Processing (NLP)
• Computer Vision
• Reinforcement Learning
• Generative AI
• Large Language Models (LLMs)
• Transformer architectures
• Retrieval-Augmented Generation (RAG)
• Prompt engineering
• Distributed training
• MLOps fundamentals
• SQL
• Git
• Docker and Kubernetes
• Cloud platforms (AWS, Azure, Google Cloud)

Soft Skills

• Research and analytical thinking
• Innovation and creativity
• Problem-solving
• Scientific writing and technical documentation
• Communication and presentation
• Collaboration across multidisciplinary teams
• Curiosity and continuous learning
• Mentoring and knowledge sharing
• Project management

Preferred Experience

• AI research laboratories
• Enterprise AI product development
• Foundation model development
• Multimodal AI systems
• AI agents and autonomous systems
• Conversational AI
• Healthcare, finance, manufacturing, or other AI-driven industries
• Open-source AI contributions

Success Metrics

• Research quality and innovation
• Publications, patents, or technical contributions
• Model performance improvements
• Successful transition of research into production
• Experiment reproducibility
• AI solution scalability and efficiency
• Contribution to organizational AI strategy
• Collaboration and stakeholder impact
• Compliance with Responsible AI principles

Nice-to-Have Skills

• AI safety and alignment research
• Explainable AI (XAI)
• Federated learning
• Graph Neural Networks (GNNs)
• Causal inference
• Synthetic data generation
• Reinforcement Learning from Human Feedback (RLHF)
• AI evaluation frameworks (DeepEval, Ragas, LangSmith)
• High-performance computing (HPC)
• Experience with open-source AI frameworks such as Hugging Face Transformers, LangChain, LlamaIndex, or vLLM