About the Role
We are seeking a skilled and detail-oriented Data Operations Engineer to support our data annotation production processes. In this role, you will play a critical part in optimizing, maintaining, and scaling our data labeling workflows, primarily using Labelbox. You will ensure that labelers are able to efficiently and accurately generate data by building tools, automating tasks, and troubleshooting complex issues within the production pipeline. Your ability to script in Python and apply engineering principles to data operations will be key to improving both efficiency and quality across our projects.
Your Day to Day
- Build, deploy, and maintain Python scripts and other tools to streamline the data annotation process, automate repetitive tasks, and reduce manual effort.
- Identify bottlenecks in the data labeling pipeline and implement solutions to enhance throughput, accuracy, and scalability of labeling operations.
- Work closely with the quality assurance team to ensure that data labeling meets accuracy standards and troubleshoot any issues related to data quality.
- Integrate and manage third-party tools with Labelbox, ensuring seamless operation and data flow across platforms.
- Provide ongoing technical support to the project managers and labelers, assisting with technical challenges in Labelbox and associated tools.
- Set up monitoring tools to track the performance of data annotation operations, reporting key metrics and areas for improvement to leadership.
About You
- Bachelor’s Degree in Engineering, Computer Science, Data Science, or a technical field.
- Proficiency in Python scripting and experience with automation of operational tasks.
- Proficiency in SQL.
- Experience with Labelbox or similar data annotation platforms.
- Strong analytical and problem-solving skills with a demonstrated ability to optimize processes.
- Experience with data pipelines and data workflow management.
- Familiarity with cloud platforms such as AWS, GCP, or Azure.
- English fluency.
Nice to Have
- Prior experience in a production or process engineering role, especially in data operations or similar environments.
- Knowledge of machine learning workflows and the data requirements for AI training.
- Understanding of project management methodologies and the ability to work collaboratively across teams.
