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Senior AI Futurist

Nexus Quantum Labs
Austin
Estimated Salary
USD 180.000 – USD 280.000
New
Live Update
13 Juli 2026
Deadline
13 Jul 2027

Job Description

Shape the future at Nexus Quantum Labs – where 2026 starts today. We're seeking a visionary Senior AI Futurist to architect intelligent systems that will redefine human-machine collaboration by 2026. Join our elite team of pioneers developing autonomous cognitive frameworks, quantum-entangled neural networks, and predictive generative AI that anticipates human needs before they arise.

In this role, you'll lead research into emergent AI paradigms while mentoring next-generation researchers. Our Austin hub offers unparalleled resources for prototyping breakthrough technologies in biocomputing, neuromorphic engineering, and ethical AI governance. Enjoy competitive equity packages, unlimited R&D budgets, and the freedom to publish groundbreaking research.

Responsibilities

  • Design and implement next-generation AI architectures for 2026 deployment
  • Lead quantum-computing-enhanced machine learning initiatives
  • Develop ethical AI frameworks for autonomous decision systems
  • Pioneer human-AI symbiosis interfaces using neuromorphic hardware
  • Collaborate with Nobel laureates on predictive generative models
  • Secure $10M+ in government innovation grants
  • Mentor PhD-level researchers in emerging AI disciplines

Qualifications

  • PhD in AI/ML or equivalent with 8+ years of research experience
  • Published work in top-tier AI conferences (NeurIPS, ICML, ICLR)
  • Expertise in quantum computing or neuromorphic systems
  • Proven track record of commercializing AI research
  • Deep understanding of ethical AI frameworks and regulations
  • Experience with billion-parameter model deployment
  • Strong background in cognitive science or neuroscience
  • Ability to secure government innovation funding

Required Skills

Quantum Computing Neuromorphic Engineering Generative AI Ethical AI Predictive Modeling Autonomous Systems Cognitive Science Quantum Machine Learning

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