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AI/ML Research Scientist - 2026 Tech Vision

Nexus Innovations
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
12 Mei 2026
Deadline
12 Mei 2027

Job Description

Join Nexus Innovations at the forefront of technological evolution as we pioneer solutions for 2026 and beyond. We're seeking an exceptional AI/ML Research Scientist to architect next-generation systems that will redefine human-machine interaction. This role offers unparalleled opportunities to work with quantum computing interfaces, neuro-symbolic AI, and autonomous decision frameworks that will shape the next decade of innovation.

Our Austin hub is a crucible of breakthrough research, where you'll collaborate with Nobel laureates and industry disruptors. We offer competitive equity packages, flexible hybrid work arrangements, and dedicated R&D budgets for your experimental projects.

Responsibilities

  • Design and implement cutting-edge AI/ML models for predictive analytics and autonomous systems
  • Lead research in quantum machine learning and neuro-symbolic integration
  • Develop ethical AI frameworks for 2026's regulatory landscape
  • Collaborate with hardware teams on neuromorphic computing implementations
  • Publish breakthrough research in top-tier AI conferences and journals
  • Mentor junior researchers and drive technical strategy for next-gen AI applications

Qualifications

  • PhD in Computer Science, AI, or related field (or equivalent research experience)
  • 5+ years in advanced ML research with 3+ years in quantum/neuro-symbolic AI
  • Published work at NeurIPS/ICML/AAAI with >500 citations
  • Expertise in PyTorch/TensorFlow and distributed computing frameworks
  • Strong background in ethical AI governance and responsible innovation
  • Experience with neuromorphic hardware (Intel Loihi, IBM TrueNorth)
  • Portfolio of deployed AI systems handling >10M daily transactions

Required Skills

AI/ML Research Quantum Computing Neuro-Symbolic AI PyTorch TensorFlow Distributed Computing Ethical AI Neuromorphic Hardware Predictive Analytics

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