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Information Technology 🏢 Full Time ⭐️ Verified

AI Ethics Research Lead

Nexus Futures Inc.
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
24 Mei 2026
Deadline
24 Mei 2027

Job Description

Shape the ethical frontier of artificial intelligence at Nexus Futures Inc. We're seeking a visionary AI Ethics Research Lead to architect responsible frameworks for 2026 and beyond. Join our multidisciplinary team at the intersection of technology, philosophy, and policy to ensure AI systems align with human values. This is your opportunity to define the conscience of tomorrow's digital ecosystems while working with Fortune 500 clients pioneering next-gen AI deployments. If you're passionate about mitigating algorithmic bias and designing transparent AI governance systems, this role will amplify your impact on the future of technology.

Responsibilities

  • Develop and implement ethical AI frameworks for autonomous systems and generative AI models
  • Lead cross-functional research on algorithmic fairness, transparency, and accountability
  • Collaborate with engineers to embed ethical safeguards into AI development lifecycles
  • Produce policy recommendations for regulatory bodies and industry standards
  • Conduct scenario planning for AI societal impact through 2030
  • Mentor junior researchers and facilitate ethics workshops with stakeholders
  • Publish peer-reviewed research on emerging AI ethical challenges

Qualifications

  • PhD in Computer Science, Philosophy, Ethics, or related field with AI focus
  • 5+ years researching AI ethics, algorithmic bias, or responsible AI
  • Expertise in regulatory frameworks (EU AI Act, NIST AI RMF)
  • Published work in top-tier AI ethics journals or conferences
  • Experience with large language model governance and safety protocols
  • Strong background in participatory design and stakeholder engagement
  • Proficiency in Python for bias auditing and fairness metrics

Required Skills

AI Ethics Algorithmic Fairness Responsible AI Governance Policy Development Research Methodology Machine Learning Bias Mitigation

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