Distribution across 10 profiles.
Middle half of Artificial Intelligence Engineers score between 29% and 35%.
0%
50%
100%
p10 · 26%
38% · p90
Task breakdown by work type
On-screen work85%
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
In-person + screen3%
Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.
Computer + action0%
Computer input, real-world output — needs someone to act on it, not just software.
Fully in-person12%
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Artificial Intelligence Engineer, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
TaskTimeTypeExposure
Writing and debugging machine learning model code (Python, PyTorch, TensorFlow)
deep expertisesocial element
31%DD
38%
Collaborating with product managers, data scientists, and engineers to define project requirements and success metrics
deep expertisesocial core
25%AA
4%
Reviewing code, documentation, and model performance from team members
deep expertisesocial core
18%DD
17%
Designing neural network architectures and experimenting with hyperparameters
deep expertisesocial element
11%DD
23%
Deploying trained models to production environments and monitoring performance drift
10%DD
49%
Preparing, cleaning, and validating training datasets
1%DD
75%
Researching new AI techniques, frameworks, and staying current with academic literature
deep expertisesocial element
1%DD
26%
TaskTimeTypeExposure
Preparing, cleaning, and validating training datasets
37%DD
46%
Designing neural network architectures and experimenting with hyperparameters
deep expertisesocial element
20%DD
27%
Collaborating with product managers, data scientists, and engineers to define project requirements and success metrics
deep expertisesocial core
15%AA
6%
Writing and debugging machine learning model code (Python, PyTorch, TensorFlow)
deep expertisesocial element
14%DD
26%
Deploying trained models to production environments and monitoring performance drift
deep expertisesocial element
6%DD
27%
Reviewing code, documentation, and model performance from team members
some context neededsocial core
4%DD
34%
Researching new AI techniques, frameworks, and staying current with academic literature
deep expertisesocial element
0%DD
26%
TaskTimeTypeExposure
Writing and debugging machine learning model code (Python, PyTorch, TensorFlow)
deep expertisesocial element
24%DD
34%
Preparing, cleaning, and validating training datasets
23%DD
55%
Researching new AI techniques, frameworks, and staying current with academic literature
deep expertisesocial element
14%DD
26%
Designing neural network architectures and experimenting with hyperparameters
deep expertisesocial element
13%DD
31%
Deploying trained models to production environments and monitoring performance drift
12%DD
52%
Collaborating with product managers, data scientists, and engineers to define project requirements and success metrics
deep expertisesocial core
7%AA
0%
Reviewing code, documentation, and model performance from team members
some context neededsocial core
4%DD
30%
Save & share
AI tools for this role
Tools relevant to the most automatable tasks in this profession.
