AI safety engineer
Also known as: alignment engineer, AI alignment researcher, responsible AI engineer
AI safety engineering covers a wide range. At frontier labs like Anthropic or OpenAI, it means research into alignment: ensuring models do what humans actually intend, even as capabilities scale. At companies deploying LLMs in products, it means building the practical controls that keep systems within bounds: evaluation frameworks, guardrails, red-team sign-off processes, and monitoring for harmful outputs.
A typical production-focused AI safety engineer designs test suites that measure model behavior across edge cases, builds fallback mechanisms for when models fail, and defines the release gates a model or prompt change has to pass before it reaches users. They work closely with security, privacy, legal, and product to translate policy into engineering requirements.
The role is genuinely niche but growing. Most builders will encounter safety engineering indirectly: through the evals their team runs, the guardrail libraries they use, or the responsible AI reviews their org requires. At labs and larger AI companies, it's a distinct career track. Elsewhere, the work tends to be distributed across AI engineers and platform teams.