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AI Safety Researcher

Neo Geneva HQ / Hybrid Zenth Advanced Research Full-Time

About the Role

Aevum Zenth operates autonomous and semi-autonomous AI systems across aerospace, robotics, financial modeling, and industrial infrastructure. As an AI Safety Researcher, you will join our cross-divisional governance and alignment team to design, test, and validate safety frameworks that ensure our models behave predictably, transparently, and within strict ethical boundaries.

You will collaborate directly with engineering leads across our Technology, Aerospace, and Robotics divisions to embed safety-by-design principles into production pipelines, conduct rigorous red-teaming exercises, and publish cutting-edge research that shapes industry standards.

Key Responsibilities

  • Develop and maintain formal verification methods, alignment benchmarks, and failure-mode analysis protocols for large-scale foundation models and multi-agent systems.
  • Design and execute red-team campaigns to identify edge-case vulnerabilities, prompt injection risks, and unintended behavioral drift in deployed AI agents.
  • Collaborate with MLOps and infrastructure teams to integrate continuous safety monitoring, automated guardrails, and real-time anomaly detection.
  • Author technical documentation, safety playbooks, and regulatory compliance reports for internal and external stakeholders.
  • Present research findings to executive leadership and cross-functional engineering councils to drive organizational AI safety maturity.
  • Contribute to peer-reviewed publications and industry working groups focused on trustworthy AI and autonomous systems governance.

Qualifications

  • Ph.D. or Master's degree in Machine Learning, Artificial Intelligence, Cognitive Science, Computer Science, or a related quantitative field.
  • 3+ years of professional experience in AI safety, model alignment, robust ML, or autonomous systems evaluation.
  • Strong proficiency in Python, PyTorch/JAX, and modern MLOps tooling (MLflow, Weights & Biases, Kubernetes).
  • Demonstrated publication record in top-tier venues (NeurIPS, ICML, ICLR, AIES, or FAccT) focusing on safety, alignment, or interpretability.
  • Experience working with LLMs, reinforcement learning, or multi-agent simulation environments.
  • Excellent technical communication skills and ability to translate complex research into actionable engineering requirements.

Preferred Qualifications

  • Background in formal verification, symbolic AI, or control theory applied to neural systems.
  • Experience with regulatory frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
  • Track record of leading cross-divisional safety initiatives or publishing open-source safety tooling.
  • Familiarity with hardware-in-the-loop testing for robotics or aerospace autonomous stacks.

How We Work

Aevum Zenth operates on a hybrid model centered around our Neo Geneva campus, with distributed teams across our global R&D hubs. You'll join a flat, research-driven culture where academic rigor meets enterprise-scale impact. We value open collaboration, iterative prototyping, and transparent communication between research and deployment teams.

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\n Home\n /\n Careers\n /\n AI Safety Researcher\n
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AI Safety Researcher

\n
\n \n \n Neo Geneva HQ / Hybrid\n \n \n \n Zenth Advanced Research\n \n \n \n Full-Time\n \n
\n
\n\n
\n

About the Role

\n

Aevum Zenth operates autonomous and semi-autonomous AI systems across aerospace, robotics, financial modeling, and industrial infrastructure. As an AI Safety Researcher, you will join our cross-divisional governance and alignment team to design, test, and validate safety frameworks that ensure our models behave predictably, transparently, and within strict ethical boundaries.

\n

You will collaborate directly with engineering leads across our Technology, Aerospace, and Robotics divisions to embed safety-by-design principles into production pipelines, conduct rigorous red-teaming exercises, and publish cutting-edge research that shapes industry standards.

\n
\n\n
\n

Key Responsibilities

\n
    \n
  • Develop and maintain formal verification methods, alignment benchmarks, and failure-mode analysis protocols for large-scale foundation models and multi-agent systems.
    • \n
  • Design and execute red-team campaigns to identify edge-case vulnerabilities, prompt injection risks, and unintended behavioral drift in deployed AI agents.
    • \n
  • Collaborate with MLOps and infrastructure teams to integrate continuous safety monitoring, automated guardrails, and real-time anomaly detection.
    • \n
  • Author technical documentation, safety playbooks, and regulatory compliance reports for internal and external stakeholders.
    • \n
  • Present research findings to executive leadership and cross-functional engineering councils to drive organizational AI safety maturity.
    • \n
  • Contribute to peer-reviewed publications and industry working groups focused on trustworthy AI and autonomous systems governance.
    • \n
\n
\n\n
\n

Qualifications

\n
    \n
  • Ph.D. or Master's degree in Machine Learning, Artificial Intelligence, Cognitive Science, Computer Science, or a related quantitative field.
    • \n
  • 3+ years of professional experience in AI safety, model alignment, robust ML, or autonomous systems evaluation.
    • \n
  • Strong proficiency in Python, PyTorch/JAX, and modern MLOps tooling (MLflow, Weights & Biases, Kubernetes).
    • \n
  • Demonstrated publication record in top-tier venues (NeurIPS, ICML, ICLR, AIES, or FAccT) focusing on safety, alignment, or interpretability.
    • \n
  • Experience working with LLMs, reinforcement learning, or multi-agent simulation environments.
    • \n
  • Excellent technical communication skills and ability to translate complex research into actionable engineering requirements.
    • \n
\n
\n\n
\n

Preferred Qualifications

\n
    \n
  • Background in formal verification, symbolic AI, or control theory applied to neural systems.
    • \n
  • Experience with regulatory frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
    • \n
  • Track record of leading cross-divisional safety initiatives or publishing open-source safety tooling.
    • \n
  • Familiarity with hardware-in-the-loop testing for robotics or aerospace autonomous stacks.
    • \n
\n
\n\n
\n

How We Work

\n

Aevum Zenth operates on a hybrid model centered around our Neo Geneva campus, with distributed teams across our global R&D hubs. You'll join a flat, research-driven culture where academic rigor meets enterprise-scale impact. We value open collaboration, iterative prototyping, and transparent communication between research and deployment teams.

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