JobFrame · Ladder · DSAISE.GEN.P5
Data Science / Artificial Intelligence / Software Engineering · General
The full level-by-level ladder — every rung side by side, current level (P5) highlighted.
Pay by level
Same pricing source as the profile's ticker header — family multiplier + inherited focus differential over the function×level base.
National-base pricing — this family's function differential is pending review, so dollar levels are not function-adjusted (level-over-level % is unaffected).
Pay rises with level — the structure that explains the most pay variance.
Mastery at this level
P5 — Proficient · good — adapts to context, gets consistent results
Aligning systems to human intent under uncertainty
- Designs objectives that keep the agent uncertain about the true goal and correctable
- Builds preference-learning loops that infer intent from human demonstrations and feedback
- Enforces measurable fairness constraints and explains group-level decisions
- Recruits ethicists, social scientists, and domain experts into technical review
What’s next — Expertat P6 → P7
Owning the system's aggregate societal consequences and the incentive landscape—not just the technical alignment of one model
What to learn
- How competitive, commercial, and geopolitical pressures distort safety incentives
- Mechanisms by which power and capability concentrate in few actors
- How emergent, unforeseen harms arise from large-scale deployment
What to practice
- Reconciling alignment and beneficence against speed-to-market demands
- Forecasting and pre-mitigating systemic and emergent risks
- Setting transparent standards that build public and institutional trust
- Designing human-augmenting deployments that preserve human autonomy
From Lead An AI / ML Research Team — the closest guide to this family (semantic match; stage bands from the canonical level binding).
No JFM factory canon (responsibilities × level, level guidelines × level, skills) for this profile yet.