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AI Research Scientist - 2026 Visionary

NeuroTech Innovations
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
23 Mei 2026
Deadline
23 Mei 2027

Job Description

Join NeuroTech Innovations at the forefront of 2026's technological revolution. We're pioneering next-generation AI systems that will redefine human-machine collaboration. As an AI Research Scientist, you'll architect breakthrough algorithms in quantum computing integration, neural network optimization, and ethical AI frameworks. Our interdisciplinary lab partners with NASA, MIT, and WHO to develop solutions for climate modeling, personalized medicine, and autonomous systems. Work in our state-of-the-art facility with unlimited compute resources and collaborate with Nobel laureates and Turing Award winners. This isn't just a job – it's your chance to shape humanity's technological trajectory.

Responsibilities

  • Lead R&D for generative AI models with 10B+ parameter architectures
  • Develop ethical frameworks for AGI alignment and bias mitigation
  • Design quantum-classical hybrid computing solutions
  • Collaborate with biotech teams on AI-driven drug discovery platforms
  • Author breakthrough research papers for Nature/Science journals
  • Mentor PhD researchers in AI ethics and computational neuroscience
  • Secure $5M+ in DARPA/NASA research grants

Qualifications

  • PhD in Machine Learning, Computer Science, or Computational Neuroscience
  • 5+ years experience in transformer architecture development
  • Published research in top-tier AI conferences (NeurIPS, ICML, ICLR)
  • Expertise in PyTorch/TensorFlow with distributed training
  • Understanding of quantum computing principles (Qiskit experience)
  • Strong background in causal inference and reinforcement learning
  • Track record of open-source contributions (GitHub with 5k+ stars)
  • Passion for ethical AI and human-centered technology design

Required Skills

Machine Learning Quantum Computing Neural Networks Ethics AI Research PyTorch Tensorflow Quantum Computing Causal Inference Reinforcement Learning

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