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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist

QuantumLeap Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 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 collaboration. As an AI Research Scientist, you'll architect breakthrough algorithms in quantum-inspired neural networks and autonomous decision-making frameworks. Our state-of-the-art facility in San Francisco offers unparalleled resources to transform theoretical models into real-world solutions. Collaborate with Nobel laureates and disrupt industry paradigms while enjoying competitive benefits and a culture that celebrates audacious innovation.

Responsibilities

  • Design and implement quantum-enhanced machine learning models for predictive analytics
  • Lead cross-functional teams in developing ethical AI governance frameworks
  • Author peer-reviewed publications on generative adversarial networks and federated learning
  • Optimize large-scale computational systems using quantum-resistant algorithms
  • Develop prototype solutions for autonomous systems in healthcare and logistics
  • Secure research grants from NSF and DARPA initiatives
  • Mentor junior researchers in emerging AI paradigms

Qualifications

  • PhD in Computer Science, Mathematics, or Physics with 5+ years of AI research
  • Expertise in PyTorch, TensorFlow, and quantum computing frameworks
  • Published research in top-tier conferences (NeurIPS, ICML, QIP)
  • Proficiency in high-performance computing environments
  • Experience with federated learning and differential privacy
  • Strong background in computational complexity theory
  • Demonstrated success in securing federal research funding
  • Fluency in Python, C++, and quantum assembly languages

Required Skills

Machine Learning Quantum Computing Neural Networks Deep Learning Python TensorFlow PyTorch Quantum Algorithms Federated Learning Research Methodology

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