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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist

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

Job Description

Join QuantumLeap Dynamics at the forefront of technological innovation as we pioneer solutions for 2026 and beyond. We're seeking visionary AI Research Scientists to develop next-generation artificial intelligence systems that will redefine industries. Our state-of-the-art lab in San Francisco offers unparalleled resources to push the boundaries of machine learning, neural networks, and quantum computing applications. Collaborate with Nobel laureates and industry disruptors in an environment where your ideas become reality. Enjoy competitive compensation, unlimited PTO, and comprehensive benefits including equity and wellness stipends.

Responsibilities

  • Design and implement cutting-edge AI algorithms for autonomous systems and predictive analytics
  • Lead research initiatives in quantum machine learning and neural architecture optimization
  • Publish findings in top-tier journals and present at international conferences
  • Mentor junior researchers and cross-functional engineering teams
  • Develop scalable prototypes for real-world deployment in healthcare, logistics, and climate tech
  • Secure federal and private research grants totaling $2M+ annually

Qualifications

  • PhD in Computer Science, AI, or related field with 3+ years industry experience
  • Published record in NeurIPS/ICML/ICLR or equivalent tier conferences
  • Expertise in PyTorch/TensorFlow and distributed computing frameworks
  • Proficiency in quantum programming languages (Qiskit, Cirq)
  • Proven ability to translate research into production-ready solutions
  • Strong background in reinforcement learning and generative models
  • Experience securing NSF/DARPA or equivalent funding

Required Skills

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

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