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2026 AI Ethics Strategist

Nexus Futures Inc.
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
29 Juni 2026
Deadline
29 Jun 2027

Job Description

Shape the ethical frontier of artificial intelligence at Nexus Futures Inc. We're seeking a visionary 2026 AI Ethics Strategist to guide our next-generation autonomous systems through the moral complexities of tomorrow. Join our cross-disciplinary team at the intersection of technology, philosophy, and policy to build AI frameworks that prioritize human dignity while driving unprecedented innovation.

This role offers the unique opportunity to influence global AI governance standards, pioneer responsible machine learning protocols, and lead high-stakes projects that will define how humanity coexists with intelligent systems by 2026.

Responsibilities

  • Develop and implement ethical frameworks for autonomous systems and generative AI models
  • Lead cross-functional audits of AI decision-making algorithms for bias mitigation
  • Collaborate with policymakers to shape international AI governance standards
  • Design transparent explainability protocols for high-stakes AI applications
  • Conduct scenario planning for emerging AI risks (e.g., existential safety, privacy erosion)
  • Mentor junior researchers in responsible AI development methodologies
  • Present findings at global tech ethics summits and industry conferences

Qualifications

  • PhD in Ethics, Philosophy, Computer Science, or related field (or equivalent experience)
  • 5+ years developing AI governance frameworks at Fortune 500 companies or research institutions
  • Deep expertise in machine learning interpretability techniques (SHAP, LIME, counterfactual analysis)
  • Published work in top-tier ethics/AI journals (e.g., Nature Machine Intelligence)
  • Experience with regulatory compliance (GDPR, EU AI Act, NIST RMF)
  • Strong background in cognitive bias research and algorithmic fairness
  • Proficiency in Python for AI ethics tool development

Required Skills

AI Ethics Machine Learning Governance Algorithmic Fairness Explainable AI Policy Development Cognitive Bias Research Regulatory Compliance AI Risk Assessment

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