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Information Technology 🏢 Full Time ⭐️ Verified

AI Ethics Researcher

Nexus Global Solutions
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Join Nexus Global Solutions at the forefront of ethical AI development. We're seeking visionary AI Ethics Researchers to shape responsible innovation frameworks for 2026 and beyond. As a pioneer in human-centered technology, you'll collaborate with world-class engineers to align AI systems with societal values while driving breakthroughs in machine learning ethics.

Why Nexus? Our dynamic team operates at the intersection of cutting-edge AI and humanistic principles. With competitive benefits, unlimited learning opportunities, and a commitment to work-life integration, we empower researchers to transform theoretical ethics into practical solutions.

Responsibilities

  • Design and implement ethical frameworks for autonomous systems and generative AI
  • Conduct interdisciplinary research on bias mitigation in machine learning algorithms
  • Develop industry standards for transparent and explainable AI deployment
  • Partner with product teams to embed ethical safeguards into development lifecycles
  • Lead cross-functional workshops on responsible AI implementation
  • Author white papers and contribute to academic journals on AI ethics
  • Advocate for ethical AI practices in regulatory discussions

Qualifications

  • PhD in Computer Science, Philosophy, Ethics, or related field with AI specialization
  • 5+ years researching algorithmic bias or AI ethics in tech or academia
  • Expertise in fairness metrics and bias mitigation techniques
  • Published work in top-tier AI ethics conferences (e.g., FAccT, NeurIPS)
  • Strong background in regulatory frameworks (EU AI Act, NIST RMF)
  • Experience with large language model governance
  • Exceptional communication skills for technical and non-technical audiences

Required Skills

AI Ethics Machine Learning Bias Mitigation Explainable AI Regulatory Compliance NIST Framework Large Language Models Algorithmic Fairness

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