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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist (2026 Vision)

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

Job Description

Shape the future of artificial intelligence at Nexus Innovations. We're seeking visionary AI Research Scientists to pioneer breakthrough technologies that will define 2026 and beyond. Join our elite team in San Francisco, where you'll collaborate with Nobel laureates and industry disruptors to solve humanity's most complex challenges through cutting-edge machine learning, quantum computing, and neural architecture innovation.

Our state-of-the-art lab offers unparalleled resources, including access to 10,000+ GPU clusters and exclusive partnerships with MIT and Stanford. Enjoy competitive equity packages, flexible remote work options, and a culture where your ideas become reality.

Responsibilities

  • Design and implement novel deep learning architectures for next-generation autonomous systems
  • Lead research initiatives in quantum machine learning and neuromorphic computing
  • Develop ethical AI frameworks ensuring alignment with 2026 regulatory standards
  • Collaborate with product teams to translate research into commercial applications
  • Publish findings in top-tier AI conferences (NeurIPS, ICML) and secure patents
  • Mentor junior researchers and foster cross-functional innovation

Qualifications

  • PhD in Computer Science, AI, or related field with 3+ years industry experience
  • Proven expertise in transformer architectures, reinforcement learning, and MLOps
  • Published record in top-tier AI journals/conferences
  • Proficiency in Python, TensorFlow/PyTorch, and distributed computing frameworks
  • Strong background in ethics, bias mitigation, and responsible AI development
  • Demonstrated ability to secure research grants ($1M+ preferred)

Required Skills

Machine Learning Deep Learning Quantum Computing Neural Networks Python TensorFlow PyTorch Research Ethics Reinforcement Learning MLOps

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