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AI Research Scientist 2026

QuantumLeap Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Join QuantumLeap Dynamics at the forefront of technological evolution! We're seeking visionary AI Research Scientists to pioneer breakthrough solutions for 2026 and beyond. Shape the future of artificial intelligence in our state-of-the-art San Francisco lab, where innovation meets impact. Collaborate with Nobel laureates and industry disruptors to develop transformative AI systems that redefine human-machine interaction.

Why QuantumLeap? We offer competitive equity packages, unlimited PTO, and a $10k annual learning stipend. Our culture celebrates intellectual curiosity and rewards bold thinking. You'll work on projects ranging from quantum-enhanced neural networks to ethical AI governance frameworks.

Responsibilities

  • Design and implement cutting-edge AI algorithms for next-generation autonomous systems
  • Lead research initiatives in quantum machine learning and neural-symbolic integration
  • Develop ethical AI frameworks aligned with 2026 regulatory standards
  • Collaborate with hardware engineers to optimize AI-quantum computing interfaces
  • Publish breakthrough research in top-tier journals and conferences
  • Mentor junior researchers and foster cross-functional innovation
  • Secure $5M+ in annual research funding through strategic proposals

Qualifications

  • PhD in Computer Science, AI, or related field with 3+ years industry experience
  • Proven expertise in transformer architectures, reinforcement learning, and generative AI
  • Published research at NeurIPS, ICML, or equivalent tier-1 conferences
  • Proficiency in PyTorch, TensorFlow, and quantum computing frameworks
  • Experience with MLOps deployment for large-scale AI systems
  • Demonstrated ability to secure competitive research grants
  • Strong background in AI ethics and responsible development practices

Required Skills

AI Research Machine Learning Quantum Computing Neural Networks Deep Learning Python PyTorch TensorFlow Ethics Reinforcement Learning MLOps

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