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AI Ethics Research Scientist

Nexus Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join Nexus Dynamics at the forefront of responsible AI development. We seek an AI Ethics Research Scientist to shape the ethical frameworks for autonomous systems in 2026. This role combines cutting-edge machine learning research with human-centered design principles to ensure our technology advances society equitably. You'll collaborate with cross-functional teams to audit algorithms, develop bias mitigation protocols, and pioneer transparent AI governance models.

What You'll Accomplish:

  • Design and implement ethical risk assessment frameworks for generative AI models
  • Lead research on explainable AI techniques for high-stakes decision systems
  • Develop bias detection and fairness metrics for deployed AI solutions
  • Create ethical guidelines for human-AI collaboration interfaces
  • Publish peer-reviewed research in top AI ethics conferences

Responsibilities

  • Conduct systematic bias audits across AI training datasets and model outputs
  • Develop prototype tools for real-time ethical compliance monitoring
  • Advise product teams on ethical design principles for AI features
  • Create stakeholder communication frameworks for algorithmic transparency
  • Lead cross-functional working groups on responsible AI deployment
  • Contribute to industry standards bodies for AI governance

Qualifications

  • PhD in Computer Science, Ethics, or related field (or equivalent experience)
  • 3+ years applied research in AI ethics or fairness-aware ML
  • Expertise in bias detection techniques for NLP/computer vision models
  • Strong publication record at venues like NeurIPS, FAccT, or ICAIL
  • Proficiency in Python, PyTorch, and fairness libraries (AIF360, Fairlearn)
  • Experience with regulatory frameworks (EU AI Act, NIST RMF)
  • Demonstrated ability to translate complex ethics concepts to technical teams

Required Skills

AI Ethics Machine Learning Algorithmic Bias Explainable AI Research Python PyTorch NLP Governance

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