We are looking for a highly skilled and motivated GenAI Applied Research and Data Scientist with a Master’s degree to join our innovative research team. The ideal candidate will have strong expertise in the latest advancements in the field of Large Language Models (LLMs), including multimodal LLMs, agentic AI, fine-tuning, distillation, inference optimization, test-time scaling, and reasoning models. This role requires a hands-on approach to research, development, and implementation of cutting-edge AI solutions.
Key Responsibilities:
- Conduct advanced research in Generative AI, focusing on the latest advancements in LLMs.
- Develop and implement advanced techniques in multimodal LLMs, agentic AI, fine-tuning, distillation, inference optimization, test-time scaling, and reasoning models.
- Collaborate with cross-functional teams to integrate AI solutions into existing and new products.
- Analyze and interpret complex datasets to drive research and development initiatives.
- Design, execute, and evaluate experiments to validate new AI models and techniques.
- Contribute to the publication of research findings in reputable journals and present at conferences.
- Mentor and guide junior researchers and data scientists.
- Stay updated on the latest trends and advancements in AI and machine learning.
Qualifications:
- Master’s degree in Artificial Intelligence or Machine Learning
- Proven expertise in the latest advancements in LLMs, including multimodal LLMs, agentic AI, fine-tuning, distillation, inference optimization, test-time scaling, and reasoning models.
- Strong hands-on experience in research and development of AI models and algorithms.
- Proficiency in programming languages such as Python, R, or Java, and experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.
- Excellent analytical and problem-solving skills.
- Ability to work independently and collaboratively in a fast-paced, dynamic environment.
- Excellent communication skills, with the ability to present complex ideas clearly and concisely.
