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Quantum AI Research Scientist 2026

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
29 Juni 2026
Deadline
29 Jun 2027

Job Description

Join Nexus Future Labs at the forefront of 2026's technological revolution. We're pioneering quantum-AI convergence systems that will redefine computational boundaries. As a Quantum AI Research Scientist, you'll develop next-gen algorithms merging quantum computing with machine learning to solve humanity's most complex challenges.

Our San Francisco hub features state-of-the-art labs and collaborative spaces where Nobel laureates and Turing Award winners mentor emerging innovators. We offer competitive equity packages, flexible work arrangements, and dedicated R&D budgets for groundbreaking projects.

Responsibilities

  • Design and implement hybrid quantum-classical ML frameworks for 2026-era applications
  • Lead cross-functional teams in developing fault-tolerant quantum neural networks
  • Author peer-reviewed research for Nature Quantum and IEEE journals
  • Optimize quantum algorithms for real-time deployment in commercial systems
  • Collaborate with hardware teams to co-design quantum-AI integrated circuits
  • Secure $2M+ in government/private research funding annually
  • Mentor postdoctoral researchers in quantum machine learning methodologies

Qualifications

  • PhD in Quantum Computing, AI, or related field with 3+ years research experience
  • Published work in top-tier quantum/AI conferences (e.g., QIP, NeurIPS)
  • Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
  • Deep understanding of quantum error correction and fault tolerance
  • Experience with large-scale ML frameworks (PyTorch, TensorFlow) at scale
  • Track record of developing novel quantum algorithms with practical applications
  • Strong background in computational complexity theory
  • Ability to translate theoretical concepts into deployable systems

Required Skills

Quantum Computing Machine Learning Python Qiskit TensorFlow Quantum Algorithms Fault Tolerance Research Methodology

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