AI Research Engineer - AI Safety
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The role
Define operational domains and assess the reliability of in-house AI capabilities.
Advance uncertainty quantification and calibration to gauge robustness in real-world and adversarial scenarios.
Design data collection and experimentation strategies to extract causal insights and support responsible decision-making.
Interface with deployed AI systems, perform scalable evaluations, and contribute to safety mechanisms and governance.
Requirements include a STEM MSc with strong mathematical/statistical background; solid software engineering in Python and/or Rust/Java/C++, and production deployment experience.
Nice-to-have items include a PhD in model evaluation/robustness or related fields, publications, and prior industrial AI safety experience.
Advance uncertainty quantification and calibration to gauge robustness in real-world and adversarial scenarios.
Design data collection and experimentation strategies to extract causal insights and support responsible decision-making.
Interface with deployed AI systems, perform scalable evaluations, and contribute to safety mechanisms and governance.
Requirements include a STEM MSc with strong mathematical/statistical background; solid software engineering in Python and/or Rust/Java/C++, and production deployment experience.
Nice-to-have items include a PhD in model evaluation/robustness or related fields, publications, and prior industrial AI safety experience.
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