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AI Ethics Research Lead (2026 Focused)

Nexus Ethical Intelligence
Austin
Estimated Salary
USD 185.000 – USD 235.000
New
Live Update
7 Juli 2026
Deadline
7 Jul 2027

Job Description

Join Nexus Ethical Intelligence at the forefront of shaping AI governance for 2026 and beyond. We're pioneering the next generation of ethical AI frameworks to ensure technology evolves responsibly. As our AI Ethics Research Lead, you'll architect the moral compass for autonomous systems that will redefine industries by 2026. Our Austin hub collaborates with global policymakers, tech innovators, and academic pioneers to build transparent, accountable AI ecosystems.

Enjoy competitive benefits including equity, flexible remote work options, and dedicated R&D time for cutting-edge projects. Help us ensure AI's 2026 trajectory aligns with humanity's best interests.

Responsibilities

  • Design and implement ethical AI governance frameworks targeting 2026 technological milestones
  • Lead cross-functional research on bias mitigation in generative AI systems
  • Develop audit protocols for autonomous decision-making algorithms
  • Collaborate with policymakers on 2026 AI regulatory standards
  • Pioneer explainability techniques for complex neural networks
  • Conduct horizon scanning for emerging ethical challenges in AI development
  • Present findings at global AI ethics summits and publish in leading journals

Qualifications

  • PhD in Computer Science, Philosophy, Ethics, or related field with 5+ years AI ethics research
  • Proven expertise in developing AI bias mitigation frameworks
  • Published work on algorithmic transparency or explainable AI
  • Experience collaborating with regulatory bodies on tech policy
  • Strong background in machine learning model development
  • Deep understanding of global AI governance landscape
  • Exceptional communication skills for technical and non-technical audiences
  • Portfolio demonstrating impact on real-world AI ethics initiatives

Required Skills

AI Ethics Machine Learning Governance Frameworks Bias Mitigation Explainable AI Regulatory Policy Research Methodology Neural Networks

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