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Senior AI Research Scientist - 2026 Vision

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

Job Description

We are looking for a visionary Senior AI Research Scientist to lead our strategic roadmap for 2026. At Nexus Horizon Labs, we are building the next generation of General Intelligence systems. If you are passionate about the future of AGI, synthetic data generation, and scalable neural architectures, this is your opportunity to define the trajectory of human-machine interaction.

In this role, you will bridge the gap between theoretical research and practical application, ensuring our proprietary models remain at the cutting edge of performance and safety. You will work in a high-performance environment with top-tier talent to solve problems that currently seem impossible.

Responsibilities

  • Lead the R&D strategy for next-generation Large Language Models (LLMs) and multimodal architectures.
  • Design and implement novel training methodologies focused on efficiency, reducing compute costs by 40% for 2026 workloads.
  • Publish high-impact research papers at top-tier conferences (NeurIPS, ICML, ICLR).
  • Collaborate with cross-functional engineering teams to integrate research findings into production-ready APIs.
  • Establish ethical guidelines and safety protocols for autonomous agent interactions.
  • Mentor junior researchers and PhD students to foster a culture of innovation.

Qualifications

  • PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
  • 5+ years of experience in deep learning, specifically with PyTorch or TensorFlow.
  • Proven track record of publishing research in top-tier venues.
  • Strong proficiency in Python, C++, and distributed systems.
  • Experience with model quantization, pruning, and hardware acceleration.
  • Deep understanding of transformer architectures and attention mechanisms.

Required Skills

Python PyTorch TensorFlow Deep Learning Machine Learning NLP Large Language Models Distributed Systems CUDA Research Methodology

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