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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist - 2026 Vision

NeuraForge Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join NeuraForge Labs at the forefront of technological evolution as we pioneer breakthrough AI systems for 2026 and beyond. We seek an exceptional AI Research Scientist to architect next-generation neural networks and machine learning frameworks that will redefine human-machine interaction. Our interdisciplinary team operates at the intersection of quantum computing, neuromorphic engineering, and sustainable AI development. This role offers unparalleled opportunities to publish groundbreaking research, collaborate with Nobel laureates, and shape the future of artificial intelligence.

Responsibilities

  • Design and implement novel deep learning architectures for real-time decision-making systems
  • Lead research initiatives in quantum-enhanced machine learning algorithms
  • Develop ethical AI frameworks aligned with 2026 regulatory standards
  • Collaborate with hardware teams to optimize AI models for next-gen processors
  • Author peer-reviewed publications and patents for transformative AI innovations
  • Mentor junior researchers through our AI Fellowship program
  • Drive cross-functional projects with neuroscience and robotics divisions

Qualifications

  • PhD in Machine Learning, Computer Science, or related field with 5+ years industry experience
  • Published research in top-tier AI conferences (NeurIPS, ICML, ICLR)
  • Expertise in transformer architectures, reinforcement learning, and federated learning
  • Proficiency in Python, PyTorch, TensorFlow, and distributed computing frameworks
  • Strong background in ethical AI development and bias mitigation techniques
  • Experience with quantum computing simulation platforms (Qiskit, Cirq)
  • Demonstrated ability to translate research into scalable production systems

Required Skills

Machine Learning Deep Learning Quantum Computing Neural Networks PyTorch TensorFlow Reinforcement Learning AI Ethics Distributed Systems

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