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AI Ethics Research Lead

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 220.000
New
Live Update
1 Juni 2026
Deadline
1 Jun 2027

Job Description

Join Nexus Labs at the forefront of ethical AI development. We're pioneering responsible AI frameworks that will shape 2026's technological landscape. As our AI Ethics Research Lead, you'll architect governance protocols for autonomous systems while collaborating with cross-functional teams to embed ethical principles into product lifecycles. This role offers unparalleled influence in defining how AI evolves within societal constraints.

We provide competitive equity packages, flexible hybrid work arrangements, and a dedicated innovation budget for cutting-edge research. Our Austin campus features state-of-the-art labs and collaborative spaces designed for breakthrough thinking.

Responsibilities

  • Develop and implement ethical AI governance frameworks for next-gen autonomous systems
  • Lead multidisciplinary teams in identifying bias mitigation strategies for predictive algorithms
  • Conduct longitudinal impact assessments of AI deployments across regulated industries
  • Author white papers and policy recommendations for federal AI ethics committees
  • Establish audit protocols for AI decision transparency in high-stakes applications
  • Mentor junior researchers in applied ethics methodologies and responsible innovation

Qualifications

  • PhD in Computer Science, Philosophy, Ethics, or related field with 5+ years AI ethics experience
  • Published research in top-tier journals on algorithmic fairness or explainable AI
  • Proven track record influencing industry-wide AI ethics standards (e.g., NIST frameworks)
  • Expertise in regulatory landscapes for autonomous systems across 3+ jurisdictions
  • Proficiency in Python with experience implementing bias detection toolkits
  • Exceptional communication skills bridging technical and policy domains
  • Portfolio demonstrating real-world ethical AI implementation at scale

Required Skills

AI Ethics Governance Algorithmic Fairness Explainable AI Policy Development Autonomous Systems Bias Mitigation Regulatory Compliance

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