Why this role matters
Were building worldclass ondevice image detection. Highquality consistently labeled data is the foundation that makes our models reliable in the real world. As a Student Assistant in Image Annotation youll create and validate the training and evaluation datasets that power our detection modelsdirectly improving accuracy robustness and user trust.
What youll do
- Annotate images with precision (e.g. bounding boxes and classes/attributes) using our labeling tools and taxonomies.
- Apply clear guidelines and edgecase rules; raise ambiguities and propose refinements to keep label definitions crisp.
- Qualityassure labels through spot checks interannotator agreement checks and consensus workflows; fix issues at speed without compromising quality.
- Curate datasets for model training/validation/test corner cases and rare classes to improve coverage.
- Support model evaluation by producing groundtruth labels for benchmarks and release testing; help compute and sanitycheck key metrics.
- Protect data privacy & security by following our datahandling procedures at all times.
What youll bring
Musthaves
- Strong visual attention to detail and consistency.
- Ability to follow written guidelines and give clear constructive feedback when something is ambiguous.
- Comfortable with repetitive tasks when neededwhile keeping quality high.
- Good written and spoken English. (Swedish is a plus not a requirement.)
- Availability 1020 hours per week during the semester; schedule can flex around exams.
What youll learn here
- How label quality and taxonomy design drive model performance and generalization.
- Practical dataset operations: curation splitting QA and eval set design.
- How ondevice/edge detection models are trained benchmarked and validated before release.
- Collaboration with AI/ML engineers product and QA in a modern MLOps pipeline.
What we offer
- Impact you can see: your labels and insights will measurably improve production model performance.
- A supportive team that values clarity inclusion and continuous learning.
- Flexible hours adapted to your studies and exam periods.
- Competitive hourly compensation.
Why this role mattersWere building worldclass ondevice image detection. Highquality consistently labeled data is the foundation that makes our models reliable in the real world. As a Student Assistant in Image Annotation youll create and validate the training and evaluation datasets that power our d...
Why this role matters
Were building worldclass ondevice image detection. Highquality consistently labeled data is the foundation that makes our models reliable in the real world. As a Student Assistant in Image Annotation youll create and validate the training and evaluation datasets that power our detection modelsdirectly improving accuracy robustness and user trust.
What youll do
- Annotate images with precision (e.g. bounding boxes and classes/attributes) using our labeling tools and taxonomies.
- Apply clear guidelines and edgecase rules; raise ambiguities and propose refinements to keep label definitions crisp.
- Qualityassure labels through spot checks interannotator agreement checks and consensus workflows; fix issues at speed without compromising quality.
- Curate datasets for model training/validation/test corner cases and rare classes to improve coverage.
- Support model evaluation by producing groundtruth labels for benchmarks and release testing; help compute and sanitycheck key metrics.
- Protect data privacy & security by following our datahandling procedures at all times.
What youll bring
Musthaves
- Strong visual attention to detail and consistency.
- Ability to follow written guidelines and give clear constructive feedback when something is ambiguous.
- Comfortable with repetitive tasks when neededwhile keeping quality high.
- Good written and spoken English. (Swedish is a plus not a requirement.)
- Availability 1020 hours per week during the semester; schedule can flex around exams.
What youll learn here
- How label quality and taxonomy design drive model performance and generalization.
- Practical dataset operations: curation splitting QA and eval set design.
- How ondevice/edge detection models are trained benchmarked and validated before release.
- Collaboration with AI/ML engineers product and QA in a modern MLOps pipeline.
What we offer
- Impact you can see: your labels and insights will measurably improve production model performance.
- A supportive team that values clarity inclusion and continuous learning.
- Flexible hours adapted to your studies and exam periods.
- Competitive hourly compensation.
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