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AI Ethics Research Scientist 2026

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 seeking an AI Ethics Research Scientist to shape the future of responsible artificial intelligence. This role combines cutting-edge machine learning with profound ethical frameworks to develop solutions that prioritize human dignity and societal benefit. Work alongside Nobel laureates and industry pioneers in our state-of-the-art research facility.

Our team pioneers breakthroughs in bias mitigation, algorithmic transparency, and human-AI collaboration systems. You'll influence global AI policy while developing proprietary frameworks for quantum-encrypted ethical audits. This position offers unparalleled opportunities to publish in top-tier journals and contribute to the IEEE's 2026 AI Standards Committee.

Responsibilities

  • Design and implement ethical frameworks for next-generation neural networks
  • Lead cross-functional teams in developing bias detection algorithms
  • Collaborate with quantum computing teams to create secure AI governance systems
  • Author whitepapers for the UN's Global AI Ethics Initiative
  • Develop real-time ethical monitoring tools for autonomous systems
  • Mentor PhD candidates in AI ethics research methodologies
  • Present findings at Davos, CES, and other global tech forums

Qualifications

  • PhD in Computer Science, Philosophy, or Ethics with focus on AI
  • 5+ years in AI research with published papers in Nature/Science
  • Expertise in federated learning and differential privacy
  • Proficiency in Python, TensorFlow, and ethical auditing frameworks
  • Experience with regulatory bodies (EU AI Act, US AI Bill of Rights)
  • Fluency in at least two programming languages beyond Python
  • Strong background in cognitive science and human-computer interaction

Required Skills

AI Ethics Machine Learning Quantum Computing Federated Learning Differential Privacy Algorithmic Auditing Policy Development

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