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Senior AI Research Engineer - 2026 Visionary

QuantumLeap Labs
San Francisco
Estimated Salary
USD 190.000 – USD 280.000
Live Update
13 Mei 2026
Deadline
13 Mei 2027

Job Description

Join QuantumLeap Labs at the forefront of technological evolution for 2026. We seek a visionary Senior AI Research Engineer to architect next-generation artificial intelligence systems that will redefine human-machine interaction. As a pioneer in quantum-entangled neural networks and autonomous cognition frameworks, you'll collaborate with Nobel laureates and disrupt industries ranging from biotech to space exploration. Our state-of-the-art facility in San Francisco offers unparalleled resources for prototyping and scaling breakthrough AI paradigms.

This role includes equity packages, unlimited learning stipends, and flexible hybrid work arrangements. We provide relocation assistance for exceptional candidates worldwide.

Responsibilities

  • Design and implement quantum-enhanced machine learning architectures for real-world deployment
  • Lead cross-disciplinary R&D teams in developing sentient AI prototypes
  • Publish groundbreaking research in top-tier journals/conferences
  • Collaborate with product teams to commercialize AI innovations
  • Mentor junior researchers and establish ethical AI governance frameworks
  • Secure patents for proprietary AI methodologies and systems

Qualifications

  • PhD in Computer Science, AI, or related field with 5+ years industry experience
  • Published research in top-tier AI/ML conferences (NeurIPS, ICML, ICLR)
  • Expertise in transformer architectures, reinforcement learning, and quantum computing
  • Proficiency in Python, TensorFlow/PyTorch, and distributed computing frameworks
  • Track record of deploying production-scale AI systems at scale
  • Experience with neuromorphic hardware and edge AI optimization
  • Demonstrated leadership in AI ethics and responsible innovation

Required Skills

Artificial Intelligence Machine Learning Quantum Computing Research Leadership Python TensorFlow Reinforcement Learning Neural Networks

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