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AI Research Scientist (2026 Vision)

QuantumLeap Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
28 Juni 2026
Deadline
28 Jun 2027

Job Description

Join QuantumLeap Labs at the forefront of 2026's technological revolution! We're pioneering next-generation AI systems that will redefine human-machine interaction. As an AI Research Scientist, you'll collaborate with Nobel laureates and industry disruptors to develop breakthrough neural architectures and quantum-inspired algorithms. Our state-of-the-art lab in San Francisco offers unparalleled resources and a culture where your ideas shape tomorrow's reality. If you're driven to solve humanity's greatest challenges through AI innovation, this is your calling.

Responsibilities

  • Design and implement cutting-edge AI models for autonomous systems and cognitive computing
  • Lead research initiatives in quantum machine learning and neural-symbolic integration
  • Publish breakthrough findings in top-tier journals and present at global conferences
  • Mentor junior researchers and foster cross-functional innovation
  • Collaborate with engineering teams to translate research into production-ready solutions
  • Secure and manage research grants from federal and private institutions
  • Participate in ethical AI frameworks development for 2026 deployment standards

Qualifications

  • PhD in Computer Science, Machine Learning, or related field (or equivalent experience)
  • 5+ years of research experience in deep learning and neural networks
  • Proven track record of publishing in NeurIPS/ICML/ICLR or equivalent venues
  • Expertise in PyTorch/TensorFlow and distributed computing frameworks
  • Strong background in quantum computing principles or reinforcement learning
  • Experience with large-scale dataset curation and MLOps pipelines
  • Demonstrated ability to secure $500K+ in research funding
  • Passion for ethical AI and responsible innovation

Required Skills

AI Research Machine Learning Deep Learning Quantum Computing Neural Networks PyTorch TensorFlow Distributed Computing Reinforcement Learning MLOps Research Funding

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