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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist - 2026 Visionary

Nexus Innovations
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
21 Mei 2026
Deadline
21 Mei 2027

Job Description

Join Nexus Innovations at the forefront of 2026's technological revolution. We're seeking an AI Research Scientist to pioneer breakthroughs in quantum machine learning and autonomous systems. Shape the future by developing scalable AI solutions that solve humanity's most pressing challenges. Collaborate with Nobel laureates and disrupt traditional paradigms in our state-of-the-art Silicon Valley laboratory.

Our award-winning team operates at the intersection of neuroscience and artificial intelligence, creating adaptive systems that learn and evolve in real-time. This role offers unparalleled opportunities to publish in top-tier journals and commercialize research through our venture incubator.

Responsibilities

  • Design and implement novel neural architectures for quantum computing environments
  • Lead cross-functional projects developing self-optimizing AI frameworks
  • Translate theoretical research into deployable production systems
  • Collaborate with robotics teams on next-gen autonomous decision-making
  • Publish findings in IEEE/ACM journals and present at global conferences
  • Secure federal grants and corporate partnerships for 2026 tech initiatives
  • Mentor PhD candidates in our AI accelerator program

Qualifications

  • PhD in Computer Science, Mathematics, or Physics with focus on ML/AI
  • 5+ years experience in quantum computing or neuromorphic systems
  • Published record in top-tier AI/ML conferences (NeurIPS, ICML)
  • Expertise in PyTorch/TensorFlow with quantum extension libraries
  • Proven ability to secure $500k+ in research funding
  • Strong background in computational complexity theory
  • Experience with federated learning and edge AI deployment

Required Skills

Quantum Computing Neuromorphic AI PyTorch TensorFlow Federated Learning Research Grant Writing Computational Complexity Autonomous Systems

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