AI Research Scientist

Description:

DealHub provides a leading solution to streamline quote-to-revenue processes. With top brands within the industry and a must-have product, we help our customers take their business to the top. We are seeking a highly skilled AI Research Scientist with a strong mathematical foundation and a PhD in a relevant field. This role focuses on transformative AI applications and the advancement of Large Language Models (LLMs). The ideal candidate will drive innovation in AI-driven transformations, developing cutting-edge methodologies that leverage deep mathematical insights for real-world AI challenges.

Key Responsibilities:

• Conduct cutting-edge research in AI, machine learning, mathematical modeling, and LLMs.
• Develop LLM-based algorithms and methodologies for AI transformation across industries.
• Collaborate with cross-functional teams to apply mathematical theories to AI, natural language processing (NLP), and LLM fine-tuning.
• Publish research findings in peer-reviewed journals and top AI/ML conferences.
• Design and implement scalable, efficient, and accurate LLM architectures for real-world applications.
• Optimize LLMs and AI algorithms for performance, robustness, and efficiency.
• Stay up to date with the latest advancements in LLMs, AI transformation, and mathematical computing.

• PhD in Mathematics, Applied Mathematics, Computer Science, or a related field.
• Expertise in LLMs (e.g., GPT-based models, transformer architectures, fine-tuning methodologies).
• Proficiency in Python, R, MATLAB, or C++, with experience in machine learning frameworks (TensorFlow, PyTorch, or Scikit-learn).
• Strong publication record in AI, LLMs, and mathematics-related journals and conferences.

Preferred Qualifications:

• Experience with research-driven AI projects focused on LLMs and generative AI.
• Familiarity with high-performance computing, distributed AI architectures, and cloud-based AI solutions.
• Ability to translate LLM research into practical applications for business transformation.
• Strong analytical and problem-solving skills, with an emphasis on LLM efficiency, interpretability, and bias mitigation.
• Effective communication and collaboration abilities in multi-disciplinary AI teams.