Job Description
We are pioneering the next era of Artificial General Intelligence (AGI) and are seeking a visionary Senior AI Research Scientist to join our elite team in San Francisco. As we accelerate towards our 2026 roadmap, you will be at the forefront of breakthrough research, designing scalable architectures that bridge the gap between current deep learning models and sentient AI systems.
At Nexus Horizon AI, we don't just predict the future of technology; we architect it. You will work with world-class engineers and data scientists to solve complex problems in natural language processing, reinforcement learning, and cognitive architectures. This is an opportunity to make a tangible impact on the trajectory of human-AI collaboration.
Why Join Us?
• Competitive equity package and top-tier compensation.
• Access to cutting-edge compute resources and proprietary datasets.
• Collaborative environment with thought leaders in the AI space.
• Focus on long-term research with high autonomy.
Responsibilities
- Lead the research and development of novel algorithms focused on achieving AGI milestones by 2026.
- Design and implement scalable deep learning models using PyTorch and TensorFlow.
- Conduct rigorous experimentation to validate new architectures and improve model performance.
- Collaborate with cross-functional teams to translate research findings into production-ready systems.
- Mentor junior researchers and PhD candidates, fostering a culture of innovation and scientific excellence.
- Publish high-impact research papers in top-tier conferences and journals.
- Stay abreast of the latest advancements in the AI field to drive continuous improvement.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field.
- 5+ years of experience in deep learning, machine learning, or a related research area.
- Strong proficiency in Python and experience with GPU acceleration (CUDA).
- Proven track record of publishing research in venues such as NeurIPS, ICML, or ICLR.
- Deep understanding of transformer architectures, attention mechanisms, and large language models.
- Experience with distributed training systems and optimizing model inference.
- Excellent communication skills and the ability to articulate complex technical concepts.