Active Learning & Data Labeling Jobs
Computer vision roles requiring Active Learning & Data Labeling expertise, across all industries and experience levels.
Open Positions
What is Active Learning & Data Labeling?
Active learning and data labelling strategy decide which examples are worth annotating, using model uncertainty, diversity or disagreement to prioritise. In applied computer vision the labelling budget is usually the binding constraint, so this is often where the biggest accuracy gains are found.
Where Active Learning & Data Labeling is used
Autonomous driving fleets collect far more footage than anyone can label, and expert annotation in medicine and industry is expensive, so choosing what to label is a first-order engineering problem.
Roles that ask for Active Learning & Data Labeling
- Machine Learning Engineer
- Data-Centric AI Engineer
- ML Operations Engineer
- Computer Vision Engineer
- Applied Scientist
Related skills & tools
Active Learning & Data Labeling jobs — common questions
Which acquisition strategies actually work?
Uncertainty sampling is the simplest and often effective, but tends to select redundant near-duplicates. Combining uncertainty with diversity or coverage criteria, and mining for genuinely novel scenarios, works better in production.
How does this relate to data-centric AI?
Closely. The broader argument — that improving data yields more than tuning architectures — has active learning, label quality auditing, and systematic error analysis as its main practical tools.
Is this a recognised job title?
Increasingly, especially at autonomy companies where data engine teams exist specifically to run the mining, labelling and retraining loop. Elsewhere it is a component of a broader ML engineering role.