Why engineering matters for AI safety
Engineering answers become things people build, wire and stand under. A model that gets a unit or a safety margin wrong produces an answer that may look complete and still fail in the world.
As models move into simulation, robotics and physical-world tasks, the gap between plausible and correct matters more. Engineers are trained to find it.
What engineering experts do
Engineers write worked solutions to design and analysis problems and rank model answers on correctness and safety. They review calculations, assumptions and the order of steps.
They evaluate agents working in simulation and robotics settings, label sensor and physical-world data, red-team for unsafe practical instructions, and produce alignment data on when a model should flag risk or defer to a qualified engineer.
- Worked engineering solutions and reasoning
- Review of calculations and assumptions
- Evaluation of simulation and robotics agents
- Labelling of sensor and physical-world data
- Red-teaming for unsafe instructions
Failure modes engineering experts catch
Engineers catch unit and conversion errors, missing safety factors, and designs that ignore how materials and loads behave. They notice when an answer would not meet the codes and standards a real project follows.
They also catch procedural risk: practical instructions that skip an isolation step, or a plan that is physically possible but unsafe to carry out.
How we vet engineering experts
Engineering candidates are interviewed by AI voice agents on how they approach design and analysis. They then complete real-world work tests in their discipline, such as checking a calculation or judging a model's answer to an engineering problem.
Credentials and experience are reviewed as part of vetting. Mechanical, electrical and civil engineers are each tested on their own discipline.