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Senior AI Safety & Ethics Architect (2026 Horizon)

Nexus Horizon Technologies
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

We are seeking a visionary Senior AI Safety & Ethics Architect (2026 Horizon) to lead our mission in aligning Artificial General Intelligence with human values. As we accelerate toward the technological breakthroughs of 2026, your role will be pivotal in ensuring our models are not only powerful but safe, transparent, and beneficial for all of humanity.

In this high-impact role, you will define the safety frameworks for next-generation AI systems, collaborate with top-tier researchers, and shape the ethical landscape of the future.

Responsibilities

  • Architect and implement robust safety protocols for Large Language Models (LLMs) and autonomous agents.
  • Conduct adversarial testing and bias audits to identify and mitigate potential risks in AI outputs.
  • Develop and refine Reward Models (RLHF) to ensure alignment with human intent.
  • Collaborate with cross-functional teams (Research, Engineering, Product) to integrate safety guardrails into production pipelines.
  • Author whitepapers and technical documentation regarding AI safety standards and compliance.
  • Advocate for responsible AI practices within the industry and at public conferences.

Qualifications

  • PhD or Master’s degree in Computer Science, Ethics, Philosophy, or a related quantitative field.
  • 5+ years of experience in AI/ML research, with a focus on safety, alignment, or policy.
  • Strong proficiency in Python, PyTorch, or TensorFlow.
  • Deep understanding of machine learning concepts including RL, NLP, and causal inference.
  • Proven track record of publishing research in top-tier AI conferences (NeurIPS, ICML, ACL).
  • Exceptional communication skills to translate complex technical concepts for diverse stakeholders.

Required Skills

AI Safety Machine Learning Ethics Python PyTorch RLHF NLP Adversarial Machine Learning Causal Inference

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