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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist

NeuroSync Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
28 Juni 2026
Deadline
28 Jun 2027

Job Description

Join NeuroSync Dynamics at the forefront of cognitive computing revolution. We're pioneering next-generation neural interfaces that will redefine human-machine collaboration by 2026. As an AI Research Scientist, you'll architect breakthrough algorithms in adaptive learning systems, contributing to projects that directly impact healthcare automation and autonomous decision frameworks.

We offer unparalleled resources including quantum-optimized computing clusters and a world-class neuroengineering lab. This is your opportunity to shape the future of artificial consciousness while working alongside Nobel laureates and Turing Award recipients in our state-of-the-art facility.

Responsibilities

  • Design and implement generative adversarial networks for synthetic neurodata simulation
  • Lead development of self-correcting reinforcement learning systems
  • Collaborate with neuroscientists to decode brain-computer interface protocols
  • Publish findings in Nature Machine Intelligence and IEEE journals
  • Mentor junior researchers in ethical AI development practices
  • Architect cross-modal learning frameworks for multi-sensory data fusion
  • Drive innovation in explainable AI for high-stakes decision systems

Qualifications

  • PhD in Computational Neuroscience or Quantum Machine Learning
  • 5+ years experience with transformer architectures and attention mechanisms
  • Expertise in PyTorch 2.0+ and quantum computing frameworks
  • Published research in top-tier AI conferences (NeurIPS, ICML)
  • Proficiency in neuromorphic computing and spiking neural networks
  • Strong background in ethical AI governance frameworks
  • Experience with federated learning on distributed quantum systems

Required Skills

Quantum Machine Learning Neuromorphic Computing Transformer Architectures PyTorch Reinforcement Learning Ethical AI Federated Learning

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