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2026 AI Ethics Research Lead

EthicaTech Innovations
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Shape the ethical frontier of AI in 2026 with EthicaTech Innovations. We're seeking a visionary Research Lead to pioneer responsible AI frameworks for autonomous systems and generative models. Join our Austin-based team at the intersection of technology and humanity, where your work will directly influence global AI governance standards.

In this high-impact role, you'll lead cross-disciplinary research teams to develop auditable ethical algorithms, conduct bias impact assessments for enterprise AI deployments, and publish seminal white papers in collaboration with top academic institutions. Our startup environment offers unparalleled autonomy to transform theoretical ethics into practical safeguards.

Responsibilities

  • Design and implement ethical AI validation frameworks for 2026-era autonomous systems
  • Lead bias mitigation initiatives for generative AI models serving 50M+ users
  • Develop industry-first explainability protocols for deep learning architectures
  • Coordinate with policy councils to shape emerging AI legislation and standards
  • Mentor researchers in responsible AI methodologies and ethical data governance
  • Present findings at global symposiums including AI Ethics Summit 2026
  • Author certification protocols for enterprise AI ethics compliance

Qualifications

  • PhD in AI Ethics, Computer Science, or Philosophy with 5+ years research experience
  • Published work in top-tier journals (Nature AI, ACM FAccT) on algorithmic bias
  • Expertise in federated learning and differential privacy techniques
  • Proficiency with Python, TensorFlow, and AI auditing toolkits
  • Experience advising Fortune 500 companies on AI governance
  • Deep understanding of EU AI Act and NIST AI Risk Management Framework
  • Track record of securing $1M+ in research grants for ethical AI initiatives

Required Skills

AI Ethics Machine Learning Governance Bias Mitigation Explainable AI Policy Development Federated Learning TensorFlow

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