Job Description
Join Nexus Horizons at the forefront of shaping humanity's technological future. We're seeking an AI Ethics & Governance Lead to architect responsible AI frameworks for 2026 and beyond. This pivotal role bridges cutting-edge innovation with ethical stewardship, ensuring our AI ecosystem advances humanity while mitigating existential risks. You'll work with Fortune 500 clients, policymakers, and research institutions to define the next generation of AI governance standards.
Our ideal candidate thrives at the intersection of technology and philosophy, possessing a unique ability to translate complex ethical principles into actionable business strategies. This hybrid-remote position offers unparalleled impact in defining how AI will reshape industries by 2026.
Responsibilities
- Develop comprehensive AI ethics frameworks for autonomous systems, generative AI, and quantum computing applications
- Lead cross-functional task forces to embed ethical guardrails into product development lifecycles
- Advise C-suite executives on regulatory compliance for emerging AI regulations (EU AI Act, US AI Bill of Rights)
- Conduct foresight analysis on societal impacts of 2026-era AI technologies
- Design audit protocols for bias detection and transparency in algorithmic decision-making
- Represent the company at global AI ethics summits and policy forums
- Mentor junior AI ethics specialists and build governance training programs
Qualifications
- Master's degree in AI Ethics, Philosophy, Computer Science, or related field (PhD preferred)
- 5+ years experience in AI governance, responsible AI, or technology ethics roles
- Deep understanding of global AI regulatory landscapes (EU AI Act, NIST AI RMF)
- Proven ability to translate ethical principles into technical requirements
- Experience with foresight methodologies and scenario planning for tech evolution
- Published work or speaking engagements on AI ethics topics
- Strong stakeholder management across technical, legal, and executive teams
- Expertise in bias mitigation techniques for ML systems