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AI Research Scientist - 2026 Vision

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

Job Description

Shape the future of artificial intelligence at QuantumLeap Labs! We're pioneering breakthroughs in 2026-era AI systems and seek visionary researchers to lead our next-gen projects. Join our elite team in San Francisco where innovation meets impact, working on cutting-edge neural networks, quantum computing applications, and autonomous systems that will redefine industries.

As an AI Research Scientist, you'll collaborate with Nobel laureates and industry disruptors to develop scalable algorithms for real-world applications. Our state-of-the-art facility offers unparalleled resources, including quantum processors and petabyte-scale datasets. We offer competitive equity packages, flexible work arrangements, and opportunities to publish groundbreaking research in top-tier journals.

Responsibilities

  • Design and implement novel machine learning architectures for 2026-era applications
  • Lead research initiatives in quantum-enhanced neural networks and autonomous systems
  • Publish peer-reviewed papers and present findings at premier AI conferences
  • Collaborate with engineering teams to translate research into production-ready solutions
  • Develop ethical frameworks for next-generation AI deployment
  • Mentor junior researchers and foster cross-functional innovation

Qualifications

  • PhD in Computer Science, AI, or related field with 3+ years research experience
  • Published work in NeurIPS/ICML/ICLR or equivalent tier conferences
  • Expertise in transformer architectures, reinforcement learning, and quantum computing
  • Proficiency in PyTorch/TensorFlow and distributed computing frameworks
  • Strong background in ethical AI alignment and bias mitigation
  • Proven track record of deploying research into production systems

Required Skills

Machine Learning Quantum Computing Neural Networks PyTorch Research Ethics Distributed Systems Reinforcement Learning

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