Job Description
We are seeking a visionary Senior AI Research Scientist to join our elite engineering division. At OmniFuture Technologies, we are not just building software; we are architecting the intelligence layer of the digital world. As we look toward the technological horizons of 2026, we need a leader who can navigate the complexities of Large Language Models (LLMs) and Generative AI.
In this role, you will define the research roadmap, pushing the boundaries of what is possible with artificial intelligence. You will work in a high-performance environment surrounded by world-class talent, with access to cutting-edge compute resources and the freedom to explore innovative architectures.
Why Join Us?
- Competitive compensation package with equity.
- Flexible remote-first culture with state-of-the-art equipment.
- Opportunity to publish in top-tier conferences (NeurIPS, ICML, ACL).
- Direct impact on products used by millions globally.
Responsibilities
- Lead the research and development of proprietary, state-of-the-art Generative AI models, specifically focusing on LLMs and diffusion models.
- Design and implement novel training pipelines, optimizing for speed, memory efficiency, and model performance.
- Collaborate with product engineering teams to translate theoretical research into scalable, deployable solutions.
- Conduct rigorous experimentation and evaluation of new architectures and fine-tuning techniques.
- Mentor junior researchers and engineers, fostering a culture of innovation and scientific excellence.
- Stay ahead of the curve on emerging trends in NLP and AI safety to ensure our technology remains compliant and robust.
Qualifications
- Ph.D. in Computer Science, Mathematics, or a related field, with a focus on Natural Language Processing (NLP), Machine Learning, or Deep Learning.
- Minimum of 5 years of experience in applied research or software engineering within the AI sector.
- Deep understanding of transformer architectures, attention mechanisms, and backpropagation.
- Proficiency in programming languages such as Python, C++, or CUDA.
- Strong mathematical foundation in linear algebra, probability, and statistics.
- Proven track record of publishing research papers in peer-reviewed venues.
- Experience with distributed training frameworks (e.g., PyTorch, TensorFlow, Ray, or Hugging Face).