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Senior AI Research Scientist (Future Tech 2026)

Nexus Horizon Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

Are you ready to define the architecture of tomorrow?

At Nexus Horizon Labs, we are building the foundational models for the year 2026 and beyond. We are seeking a visionary Senior AI Research Scientist to lead our Frontier Model Alignment division.

In this pivotal role, you won't just build models; you will architect the ethical framework and safety protocols that will govern Artificial General Intelligence (AGI). You will work at the intersection of deep learning, cognitive science, and policy to ensure our technology benefits humanity.

Why Join Us?

  • Work on the bleeding edge of AI safety and interpretability.
  • Competitive compensation package with equity options.
  • Flexible remote-first policy with access to premium SF amenities.
  • Access to state-of-the-art compute clusters and research grants.

Responsibilities

  • Design and implement novel research methodologies for AI interpretability and safety.
  • Lead a team of researchers in developing scalable alignment techniques for next-generation LLMs.
  • Publish breakthrough papers in top-tier conferences (NeurIPS, ICML, ICRA).
  • Collaborate with legal and policy teams to navigate the regulatory landscape of AI in 2026.
  • Optimize model performance for edge computing environments and latency-sensitive applications.

Qualifications

  • Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field.
  • 5+ years of experience in deep learning, reinforcement learning, or natural language processing.
  • Proven track record of publishing research in peer-reviewed venues.
  • Strong programming skills in Python, PyTorch, or TensorFlow.
  • Experience with large-scale distributed training and model optimization.
  • Passion for the ethical implications of AI and long-term safety research.

Required Skills

Python PyTorch Deep Learning NLP AI Safety Reinforcement Learning Research Distributed Systems Machine Learning

Ready to Take This Challenge?

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