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Information Technology 🏢 Full Time ⭐️ Verified

AI Ethics Research Lead

Nexus Future Labs
Austin
Estimated Salary
USD 160.000 – USD 220.000
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join Nexus Future Labs at the forefront of ethical AI development. We're pioneering responsible machine learning systems that will define the technological landscape of 2026 and beyond. As our AI Ethics Research Lead, you'll shape frameworks ensuring AI innovation aligns with human values while driving breakthroughs in autonomous systems and predictive analytics.

Located in Austin's thriving tech corridor, we offer competitive benefits, flexible work arrangements, and the opportunity to collaborate with Nobel laureates and Turing Award winners. Our culture emphasizes intellectual curiosity and societal impact.

Responsibilities

  • Design and implement ethical governance frameworks for next-generation AI systems
  • Lead cross-functional teams in auditing algorithmic bias and fairness metrics
  • Develop predictive models for societal impact assessment of emerging technologies
  • Partner with policymakers to establish industry standards for AI deployment by 2026
  • Present research findings at global ethics conferences and publish in top-tier journals
  • Mentor junior researchers in responsible AI methodologies
  • Oversee compliance with evolving international AI regulatory landscapes

Qualifications

  • PhD in Computer Science, Ethics, Philosophy, or related field
  • 5+ years of experience in AI ethics research or policy development
  • Expertise in fairness metrics, bias mitigation, and explainable AI techniques
  • Proven track record of publishing in top-tier ethics/AI conferences
  • Deep understanding of global AI regulatory frameworks (EU AI Act, NIST RMF)
  • Experience leading multidisciplinary research teams
  • Strong background in predictive modeling and statistical analysis
  • Exceptional communication skills for technical and non-technical audiences

Required Skills

AI Ethics Machine Learning Algorithmic Fairness Predictive Modeling Research Leadership Policy Development Explainable AI Statistical Analysis

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