Job Description
Join the Pioneers of 2026.
Nexus Future Labs is at the forefront of the next industrial revolution. We are seeking a visionary Senior AI Research Scientist to lead our Generative AI division. In 2026, we aren't just predicting the future; we are engineering it. You will be instrumental in developing the foundational models that will define human-machine interaction for the next decade.
Our mission is to bridge the gap between artificial general intelligence and practical, safe, and beneficial applications. If you are passionate about pushing the boundaries of LLMs, diffusion models, and multimodal learning, this is your opportunity to shape the technology of tomorrow.
Why Join Us?
• Work in a state-of-the-art facility in the heart of the Bay Area.
• Access to top-tier compute resources and a diverse team of global experts.
• Competitive compensation package reflecting your impact on the future.
Responsibilities
- Architect Design: Lead the research and development of novel neural network architectures tailored for high-performance generative tasks.
- Model Optimization: Spearhead efforts to optimize model inference speed and reduce computational costs for large-scale deployment.
- Publication & Thought Leadership: Author high-impact research papers for top-tier conferences (NeurIPS, ICML, ICLR) and establish Nexus as a leader in the AI space.
- Mentorship: Guide a team of junior data scientists and ML engineers, fostering a culture of innovation and continuous learning.
- Cross-Functional Collaboration: Partner with product engineering teams to translate theoretical research into scalable, production-ready software solutions.
- Ethical AI: Ensure all models adhere to strict safety guidelines, bias mitigation protocols, and alignment with human values.
Qualifications
- Education: PhD in Computer Science, Mathematics, Statistics, or a related field with a focus on Artificial Intelligence.
- Experience: Minimum of 5+ years of experience in deep learning, specifically within the Generative AI or Natural Language Processing space.
- Technical Skills: Proficiency in programming languages such as Python, PyTorch, or TensorFlow.
- Knowledge Base: Deep understanding of Transformer architectures, Attention mechanisms, and Reinforcement Learning from Human Feedback (RLHF).
- Research Record: A strong track record of peer-reviewed publications in recognized AI conferences or journals.
- Problem Solving: Demonstrated ability to tackle complex, open-ended research problems with innovative solutions.