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Information Technology 🏢 Full Time ⭐️ Verified

AI Ethics & Governance Specialist

FutureGuardian Systems
Austin
Estimated Salary
USD 120.000 – USD 180.000
Live Update
16 Mei 2026
Deadline
16 Mei 2027

Job Description

Join FutureGuardian Systems at the forefront of responsible AI development. As a pioneer in ethical technology deployment, we're seeking an AI Ethics & Governance Specialist to shape the future of transparent, accountable artificial intelligence. You'll architect frameworks that ensure our autonomous systems align with human values while driving innovation in healthcare, climate modeling, and predictive analytics. This role offers hybrid work arrangements, competitive equity packages, and opportunities to present at global AI ethics summits.

Responsibilities

  • Develop and implement ethical AI governance frameworks compliant with evolving regulations
  • Conduct bias audits and fairness assessments on machine learning models
  • Lead cross-functional stakeholder workshops on responsible AI deployment
  • Design transparency protocols for high-stakes autonomous systems
  • Author impact assessments for AI-driven decision-making tools
  • Collaborate with product teams to embed ethical considerations into development lifecycles
  • Monitor emerging AI legislation and update compliance strategies accordingly

Qualifications

  • Master's degree in Ethics, Philosophy, Computer Science, or related field
  • 5+ years experience in AI governance or responsible technology roles
  • Proficiency with bias detection tools (IBM AI Fairness 360, Aequitas)
  • Deep understanding of EU AI Act, NIST AI RMF, and OECD AI Principles
  • Experience conducting algorithmic impact assessments for regulated industries
  • Strong background in participatory design methods with diverse communities
  • Certification in AI Ethics (e.g., IEEE, EIT Digital) preferred
  • Published work on AI ethics or technology policy highly valued

Required Skills

AI Ethics Governance Frameworks Bias Auditing Regulatory Compliance Stakeholder Engagement Algorithmic Impact Assessment Machine Learning Transparency Participatory Design

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