Job Description
Are you ready to shape the ethical landscape of Artificial Intelligence in 2026 and beyond? Quantum Horizon Labs is seeking a visionary Lead AI Safety Engineer to spearhead the development of safe, reliable, and beneficial AGI systems.
In this pivotal role, you will bridge the gap between cutting-edge machine learning research and real-world application safety. You will define the safety protocols for our next-generation language models, ensuring they operate within ethical boundaries while delivering unprecedented value.
We are looking for a strategic thinker who is passionate about the future of technology and dedicated to mitigating risks in automated systems.
Responsibilities
- Architect Safety Frameworks: Design and implement comprehensive safety guidelines, risk assessments, and mitigation strategies for Large Language Models (LLMs) and autonomous agents.
- Red Teaming & Adversarial Testing: Lead complex adversarial testing campaigns to identify vulnerabilities, jailbreaks, and safety violations in AI models before deployment.
- Policy Governance: Collaborate with legal, ethics, and compliance teams to establish industry-leading AI governance standards and internal policies.
- Model Alignment: Work closely with research scientists to ensure model outputs align with human values, reduce bias, and adhere to strict safety constraints.
- Real-Time Monitoring: Develop and deploy continuous monitoring systems to detect and correct model drift or unintended behaviors in live production environments.
- Stakeholder Communication: Translate complex technical safety concepts into clear, actionable insights for executive leadership and non-technical stakeholders.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, Philosophy, or a related technical field (or equivalent practical experience).
- Experience: 5+ years of experience in Machine Learning, AI Safety, Applied Research, or a related technical discipline.
- Technical Proficiency: Strong proficiency in Python, PyTorch, or TensorFlow, with deep understanding of NLP and transformer architectures.
- Knowledge Base: Familiarity with AI safety literature, causal reasoning, interpretability, and alignment research methodologies.
- Problem Solving: Demonstrated ability to debug complex systems and solve ambiguous safety challenges under tight deadlines.
- Soft Skills: Exceptional verbal and written communication skills, with the ability to influence cross-functional teams.