Full-Time

ML/OpenCV Data Labeler

Turing • Remote

Company

Turing

Hourly rate

$20 – $45/hr

Location

Remote

Listed

25d ago

Field:: Data Science

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About this Role

We’re looking for a Machine Learning / Computer Vision Data Labeler to support customers onboarding and build high-quality training datasets for our computer vision products used in manufacturing environments. This role sits at the intersection of ML data operations and light product/customer work—you’ll help us understand what customers do on the factory floor, collect and analyze representative sample data from each station, and translate real-world processes into clear labeling instructions and reliable datasets.
This is not a super-senior role, but it does require strong ownership, attention to detail, and comfort working with highly confidential customer data.

What you'll do

Coordinate and execute sample data capture across all manufacturing stations, ensuring coverage of real-world variation
Work with our on-site implementation team to validate camera setup outputs (camera position, field of view, recording settings, connectivity, sample clips/images).
Organize, clean, and curate datasets (images/video), including selecting representative samples, filtering unusable footage, and documenting capture conditions.
Perform data labeling/annotation for computer vision tasks (e.g., classification, object detection, segmentation, defect tagging, action/process step labeling—depending on the use case).
Create and maintain labeling taxonomies and annotation guidelines that are consistent, scalable, and easy for others to follow.
Run quality checks (spot checks, consistency reviews, edge-case handling) and partner with ML/Engineering to continuously improve label quality.
Conduct lightweight exploratory analysis on incoming datasets (e.g., distributions, coverage gaps, common failure modes, ambiguity hot-spots).
Flag data issues early (missing stations, misaligned camera views, insufficient examples, inconsistent definitions) and propose fixes.
Provide structured feedback to ML and product teams: what data we have, what we’re missing, and what will improve model performance.
Support customer onboarding by learning what the client does, mapping their workflow/stations, and translating their needs into data/labeling requirements.
Communicate clearly with internal stakeholders and occasionally with customers to align on labeling definitions, success criteria, timelines, and data handling expectations.
Document processes, station definitions, and dataset decisions so teams can move fast and stay aligned.
Work with sensitive/secret customer manufacturing data and follow strict security policies (access control, secure transfer/storage, need-to-know practices, and customer-specific handling requirements).

What you need

1–4 years of experience in a role involving data labeling/annotation, ML data operations, computer vision datasets
Working knowledge of computer vision fundamentals (classification vs detection vs segmentation; what labels are used for; why consistency matters).
Experience with labeling tools such as CVAT, Labelbox, V7, Supervisely, or similar (or the ability to learn quickly).
Comfort working with data formats/workflows (e.g., CSV/JSON annotations, COCO-style formats, dataset folders, basic versioning concepts).
Strong written and verbal communication skills; able to explain labeling decisions and customer workflows clearly.
Professional maturity and discretion—ability to handle highly confidential customer data.
German language ok, strong communication in English preferred

Nice to have

Exposure to manufacturing environments (industrial processes, station-based workflows, quality inspection).
Familiarity with camera systems / video capture pipelines (e.g., frame rate, resolution trade-offs, lighting impacts, field of view).