Distribution across 36 profiles.
Middle half of Machine Learning Engineers score between 32% and 40%.
0%
50%
100%
p10 · 29%
42% · p90
Task breakdown by work type
On-screen work67%
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
In-person + screen27%
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-person7%
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Machine Learning Engineer, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
TaskTimeTypeExposure
Designing and implementing machine learning models (e.g., neural networks, decision trees) to solve specific business or research problems, including selecting algorithms, tuning hyperparameters, and validating performance.
deep expertisesocial element
23%DD
26%
Collaborating with software engineers to deploy trained models into production environments, ensuring scalability, latency, and reliability (e.g., using APIs, microservices, or edge devices).
some context neededsocial core
22%AD
8%
Researching and experimenting with new machine learning techniques, tools, or frameworks (e.g., reading papers, attending conferences, or prototyping novel approaches).
deep expertisesocial element
22%AD
11%
Preprocessing and cleaning large datasets to prepare them for training, including handling missing values, normalizing data, and feature engineering.
13%DD
64%
Writing documentation for models, pipelines, and experiments to ensure reproducibility and knowledge sharing with team members.
8%DD
52%
Meeting with stakeholders (e.g., product managers, data scientists, or business teams) to understand requirements, present results, or align on project goals.
deep expertisesocial core
7%AA
6%
Monitoring and maintaining deployed models to detect performance degradation, data drift, or concept drift, and retraining models as needed.
0%DD
44%
Debugging and optimizing code (e.g., Python, TensorFlow, PyTorch) to improve model training speed, reduce memory usage, or fix errors in pipelines.
0%DD
61%
TaskTimeTypeExposure
Designing and implementing machine learning models (e.g., neural networks, decision trees) to solve specific business or research problems, including selecting algorithms, tuning hyperparameters, and validating performance.
deep expertisesocial element
25%DD
31%
Preprocessing and cleaning large datasets to prepare them for training, including handling missing values, normalizing data, and feature engineering.
18%DD
60%
Meeting with stakeholders (e.g., product managers, data scientists, or business teams) to understand requirements, present results, or align on project goals.
deep expertisesocial core
12%AA
1%
Collaborating with software engineers to deploy trained models into production environments, ensuring scalability, latency, and reliability (e.g., using APIs, microservices, or edge devices).
some context neededsocial core
10%AD
25%
Writing documentation for models, pipelines, and experiments to ensure reproducibility and knowledge sharing with team members.
10%DD
47%
Monitoring and maintaining deployed models to detect performance degradation, data drift, or concept drift, and retraining models as needed.
7%DD
44%
Debugging and optimizing code (e.g., Python, TensorFlow, PyTorch) to improve model training speed, reduce memory usage, or fix errors in pipelines.
7%DD
71%
Researching and experimenting with new machine learning techniques, tools, or frameworks (e.g., reading papers, attending conferences, or prototyping novel approaches).
deep expertisesocial element
7%AD
8%
TaskTimeTypeExposure
Preprocessing and cleaning large datasets to prepare them for training, including handling missing values, normalizing data, and feature engineering.
23%DD
61%
Designing and implementing machine learning models (e.g., neural networks, decision trees) to solve specific business or research problems, including selecting algorithms, tuning hyperparameters, and validating performance.
deep expertisesocial element
20%DD
24%
Writing documentation for models, pipelines, and experiments to ensure reproducibility and knowledge sharing with team members.
15%DD
76%
Monitoring and maintaining deployed models to detect performance degradation, data drift, or concept drift, and retraining models as needed.
14%DD
54%
Researching and experimenting with new machine learning techniques, tools, or frameworks (e.g., reading papers, attending conferences, or prototyping novel approaches).
deep expertisesocial element
10%AD
10%
Debugging and optimizing code (e.g., Python, TensorFlow, PyTorch) to improve model training speed, reduce memory usage, or fix errors in pipelines.
9%DD
66%
Meeting with stakeholders (e.g., product managers, data scientists, or business teams) to understand requirements, present results, or align on project goals.
deep expertisesocial core
7%AA
0%
Collaborating with software engineers to deploy trained models into production environments, ensuring scalability, latency, and reliability (e.g., using APIs, microservices, or edge devices).
some context neededsocial core
0%AD
16%
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AI tools for this role
Tools relevant to the most automatable tasks in this profession.
