icims
AI Specialist (EHS)
Ideagen
Ruddington, GBR
Full-time
Mid
Hybrid
Discovered 1 weeks ago
Free
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Benefits -
- Benefits at Ideagen
Salary - To be discussed at next stage
- Ideagen is building AI for environments where safety, accuracy and professional judgement truly matter.
- As an AI EHS Specialist, you’ll apply your real‑world EHS experience to shape how AI systems understand, assess and respond to safety‑critical scenarios. This role is designed for practitioners who have worked operationally - investigating incidents, managing permits, assessing risk, and making decisions that directly affect worker safety and now want to apply that expertise at scale.
- Rather than managing EHS within a single organisation, you’ll help define what good and safe practice looks like for an AI platform used by thousands of safety professionals. Your judgement will directly influence how models classify incidents, assess serious injury and fatality (SIF) risk, validate permits‑to‑work, and interpret regulatory requirements.
- You don’t need AI experience for this role. What matters is operational credibility, strong professional judgement, and the ability to evaluate decisions rigorously and consistently.
- Label EHS training data in line with defined annotation guidelines, including incident classification, permit‑to‑work validation, SIF severity assessment, and hazard tagging
- Evaluate AI model outputs during testing cycles, scoring responses against expert rubrics and clearly documenting what the correct EHS response should be and why
- Test AI capabilities against realistic, high‑risk operational scenarios using the standard a competent EHS professional would apply in practice
- Contribute to EHS red‑team testing by identifying edge cases, unusual incident patterns, regulatory overlaps, and scenarios likely to expose model weaknesses
- Support the curation of the EHS regulatory corpus by identifying gaps, flagging outdated guidance, and recommending authoritative sources
- Participate in calibration sessions to maintain consistency across labelling and evaluation decisions, resolving differences through clear, evidence‑based reasoning
- Work closely with AI engineers and domain leads, translating practitioner feedback into concrete improvements in model behaviour
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