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AI Ethicist - 2026 Vision

Nexus Innovations
Austin, TX
Estimated Salary
USD 140.000 – USD 220.000
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

Join Nexus Innovations at the forefront of ethical AI development for 2026. We're seeking visionary AI Ethicists to shape responsible AI frameworks that will define the next generation of technology. This role combines cutting-edge machine learning with profound philosophical rigor to ensure our innovations benefit humanity. You'll collaborate with world-class researchers and engineers to build ethical guardrails for autonomous systems, predictive analytics, and neural interfaces. Our Austin headquarters offers a dynamic environment where your expertise directly influences how AI evolves in critical sectors like healthcare, climate science, and urban planning.

Responsibilities

  • Design and implement ethical frameworks for generative AI and predictive modeling systems
  • Conduct bias audits and fairness assessments across AI training datasets
  • Develop corporate governance policies for AI deployment in regulated industries
  • Lead interdisciplinary workshops with engineers, policymakers, and social scientists
  • Author whitepapers on AI ethics for regulatory bodies and industry consortiums
  • Monitor emerging AI regulations (e.g., EU AI Act, state-level legislation) for compliance
  • Advocate for ethical AI principles in public forums and industry conferences

Qualifications

  • PhD in Philosophy, Computer Science, Ethics, or related field with 5+ years AI ethics experience
  • Published research on algorithmic bias, fairness metrics, or explainable AI
  • Expertise in regulatory frameworks (GDPR, NIST AI RMF, EU AI Act)
  • Proficiency in Python/R for bias auditing tool development
  • Experience with stakeholder engagement across technical and non-technical audiences
  • Knowledge of neuroethics and human-AI interaction principles
  • Track record of influencing AI policy development or corporate governance

Required Skills

AI Ethics Algorithmic Bias Machine Learning Regulatory Compliance Explainable AI Governance Policy Stakeholder Engagement Bias Auditing

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