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AI Research Scientist - Future of Intelligence (2026)

Nexus Horizon Labs
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
Live Update
17 Mei 2026
Deadline
17 Mei 2027

Job Description

We are seeking a visionary AI Research Scientist to join our elite team at Nexus Horizon Labs. As we prepare to launch our flagship '2026 Initiative,' we are looking for pioneers who are not just building the technology of today, but defining the intelligence of tomorrow.

In this role, you will lead the research and development of next-generation Generative AI models and autonomous agents. You will work on solving complex, unsolved problems in reasoning, multimodal learning, and human-AI alignment. This is a high-impact position for a researcher who wants to leave a legacy in the tech landscape.

Why Join Us?
We offer a competitive compensation package, equity, and the unique opportunity to shape the future of technology. Our state-of-the-art facility in San Francisco is home to world-class researchers and engineers working on the bleeding edge of AI.

Responsibilities

  • Lead research initiatives focused on scaling Generative AI models for 2026 and beyond.
  • Design and implement novel deep learning architectures to improve model efficiency and accuracy.
  • Collaborate with cross-functional teams of engineers, ethicists, and product designers to deploy safe and effective AI solutions.
  • Publish high-impact research papers and present findings at top-tier global conferences.
  • Mentor junior researchers and PhD candidates within the Innovation Lab.
  • Conduct rigorous testing and validation of AI systems to ensure robustness and reliability.

Qualifications

  • Ph.D. or Master’s degree in Computer Science, Mathematics, Physics, or a related quantitative field.
  • 5+ years of experience in machine learning, deep learning, or natural language processing.
  • Strong proficiency in Python, PyTorch, or TensorFlow.
  • Proven track record of publishing in top-tier venues (NeurIPS, ICML, ICLR, ACL).
  • Experience with large-scale model training and fine-tuning (LLMs, diffusion models).
  • Deep understanding of machine learning theory and optimization algorithms.

Required Skills

Python PyTorch TensorFlow Deep Learning Machine Learning Natural Language Processing (NLP) Large Language Models (LLMs) Generative AI Research PhD Stanford MIT Berkeley San Francisco

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