Avride
Overview
We are looking for a
Data Annotation Engineer
who will focus on testing and optimizing labeling workflows. You will be responsible for developing and refining annotation pipelines, identifying and resolving issues in 3D annotation tools, and creating metrics to assess data quality. Your work will directly impact how efficiently our annotation team produces accurate and high-quality data for training machine learning models. We use
Python
and
ClickHouse , and we’d love to hear your ideas on how to use them effectively.
Responsibilities
Develop and maintain 3D data labeling pipelines.
Collaborate with backend and frontend teams to improve interface usability and the data delivery pipeline.
Work with external vendors to ensure consistent data quality standards.
Testing & Troubleshooting
Test and evaluate annotation tools to identify bugs, UX issues, and performance bottlenecks.
Perform various types of testing (functional, regression, exploratory) on new features.
Work closely with developers and product managers to promptly resolve issues.
Quality & Analytics
Create and monitor annotation quality metrics, analyze trends, and recommend improvements.
Document workflows, best practices, and edge cases for 3D point cloud labeling.
Use Python and ClickHouse to analyze data, monitor performance, and support debugging.
Qualifications
A degree in a relevant field (Computer Science, QA, or other technical disciplines).
Understanding of testing processes (functional, regression, smoke, UI/UX) and experience writing clear bug reports.
Python skills for data processing and analysis (Pandas, NumPy).
Ability to query databases (experience with ClickHouse is a plus).
Nice to Have
Hands-on experience with LiDAR point clouds;
Hands-on experience in data annotation or data visualization (preferably with 3D datasets);
Experience using semi-automated labeling tools or active learning methods.
Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.
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Data Annotation Engineer
who will focus on testing and optimizing labeling workflows. You will be responsible for developing and refining annotation pipelines, identifying and resolving issues in 3D annotation tools, and creating metrics to assess data quality. Your work will directly impact how efficiently our annotation team produces accurate and high-quality data for training machine learning models. We use
Python
and
ClickHouse , and we’d love to hear your ideas on how to use them effectively.
Responsibilities
Develop and maintain 3D data labeling pipelines.
Collaborate with backend and frontend teams to improve interface usability and the data delivery pipeline.
Work with external vendors to ensure consistent data quality standards.
Testing & Troubleshooting
Test and evaluate annotation tools to identify bugs, UX issues, and performance bottlenecks.
Perform various types of testing (functional, regression, exploratory) on new features.
Work closely with developers and product managers to promptly resolve issues.
Quality & Analytics
Create and monitor annotation quality metrics, analyze trends, and recommend improvements.
Document workflows, best practices, and edge cases for 3D point cloud labeling.
Use Python and ClickHouse to analyze data, monitor performance, and support debugging.
Qualifications
A degree in a relevant field (Computer Science, QA, or other technical disciplines).
Understanding of testing processes (functional, regression, smoke, UI/UX) and experience writing clear bug reports.
Python skills for data processing and analysis (Pandas, NumPy).
Ability to query databases (experience with ClickHouse is a plus).
Nice to Have
Hands-on experience with LiDAR point clouds;
Hands-on experience in data annotation or data visualization (preferably with 3D datasets);
Experience using semi-automated labeling tools or active learning methods.
Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.
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