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Information Technology 🏢 Full Time ⭐️ Verified

AI Ethics Research Lead

FutureGuard AI
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join FutureGuard AI at the forefront of responsible AI development. We're seeking visionary AI Ethics Research Leads to shape the ethical frameworks for next-generation autonomous systems. Lead groundbreaking research at our state-of-the-art labs while collaborating with Nobel laureates and Fortune 500 partners to ensure AI advances humanity's progress without compromising safety or equity.

This hybrid role offers unmatched impact on global AI policy development, with opportunities to publish in top-tier journals and testify before congressional committees. Our competitive compensation package includes equity, unlimited PTO, and sabbatical programs.

Responsibilities

  • Design and implement ethical AI governance frameworks for autonomous systems
  • Lead cross-functional research teams in bias mitigation and fairness audits
  • Develop predictive models for AI system societal impact assessment
  • Collaborate with policymakers to shape federal AI legislation
  • Present findings at global AI ethics conferences and summits
  • Author peer-reviewed publications on AI safety and alignment research
  • Establish industry-wide benchmarks for transparent AI decision-making

Qualifications

  • PhD in AI Ethics, Machine Learning, or related field (or equivalent experience)
  • 5+ years leading AI ethics research at Fortune 500 or top-tier institutions
  • Published work in Nature, Science, or equivalent high-impact journals
  • Expertise in federated learning and differential privacy techniques
  • Deep understanding of EU AI Act and NIST AI RMF frameworks
  • Experience testifying before legislative bodies or regulatory agencies
  • Proficiency in Python, PyTorch, and causal inference libraries

Required Skills

AI Ethics Machine Learning Causal Inference Federated Learning Policy Development Research Leadership Differential Privacy

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