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AI Research Scientist (2026 Foresight)

QuantumLeap Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
11 Mei 2026
Deadline
11 Mei 2027

Job Description

Join our pioneering team at QuantumLeap Labs to architect the future of artificial intelligence. We're seeking visionary researchers to develop next-gen AI systems that will redefine industries by 2026. This role combines cutting-edge machine learning, quantum computing principles, and ethical AI frameworks to solve humanity's grand challenges.

What you'll impact: Your work will directly shape autonomous systems, climate modeling solutions, and personalized healthcare AI that will touch billions of lives. We offer unparalleled resources, collaborative innovation spaces, and a culture that rewards intellectual curiosity.

Responsibilities

  • Lead R&D for next-generation AI architectures with 10-year foresight
  • Develop quantum-enhanced machine learning models for complex problem-solving
  • Collaborate with Nobel laureates on AI ethics and alignment frameworks
  • Author breakthrough research papers for Nature/Science journals
  • Mentor PhD researchers in AI safety and explainable AI methodologies
  • Prototype AI systems with 100+ parameter scales
  • Drive innovation in neuromorphic computing and quantum neural networks

Qualifications

  • PhD in Computer Science, AI, or related field with quantum computing specialization
  • 5+ years developing production-scale AI systems at FAANG or equivalent
  • Published research in top-tier AI/ML conferences (NeurIPS, ICML)
  • Expertise in transformer architectures, reinforcement learning, and diffusion models
  • Strong background in theoretical physics or computational neuroscience
  • Proven track record of ethical AI implementation
  • Experience with quantum programming frameworks (Qiskit, Cirq)
  • Fluency in Python, PyTorch, and distributed computing systems

Required Skills

AI Research Quantum Computing Machine Learning Neural Networks Ethics AI Python PyTorch Quantum Programming Reinforcement Learning

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