Job Description
Nexus Future Systems is pioneering the next generation of Artificial Intelligence, specifically targeting the development and deployment of the GPT-5 model roadmap slated for 2026. We are looking for a visionary Senior AI Research Scientist to lead our breakthrough research in Large Language Models (LLMs), Natural Language Processing (NLP), and multimodal reasoning.
In this role, you will be at the forefront of the 2026 AI revolution, working alongside world-class engineers and data scientists to push the boundaries of what is possible in generative AI. If you are passionate about building AGI-ready systems and want to shape the future of human-computer interaction, we want to hear from you.
Responsibilities
- Lead the architectural design and training of the next-generation GPT-5 model, focusing on scalability and reasoning capabilities.
- Oversee the fine-tuning process using Reinforcement Learning from Human Feedback (RLHF) to enhance model alignment and safety.
- Design and implement novel training pipelines, optimizing for reduced latency and higher throughput on massive GPU clusters.
- Collaborate with cross-functional teams to integrate advanced AI models into production applications.
- Conduct cutting-edge research in prompt engineering, few-shot learning, and chain-of-thought reasoning techniques.
- Establish rigorous evaluation metrics to measure model performance against state-of-the-art benchmarks.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field with a focus on Machine Learning/AI.
- 5+ years of research experience in Deep Learning, specifically working with Transformer architectures (BERT, GPT, T5).
- Extensive experience in PyTorch or TensorFlow, with a proven track record of publishing in top-tier conferences (NeurIPS, ICML, ACL).
- Strong proficiency in Python, CUDA, and distributed computing frameworks (Ray, MPI).
- Deep understanding of NLP tasks including text generation, summarization, and question answering.
- Experience with model optimization techniques such as quantization, pruning, and distillation.