Data Operations Engineer

Shape the Future of AI

At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.

About Labelbox

We're the only company offering three integrated solutions for frontier AI development:

1. Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
2. Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
3. Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling

Why Join Us

• High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
• Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
• Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
• Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
• Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.

Role Overview

We are seeking a skilled and detail-oriented Data Operations Engineer to support our data annotation and data quality assurance 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 human-labeled data by building tools, using LLM models, automating common project management tasks, and troubleshooting complex issues within the production pipeline. Your ability to script in Python and apply engineering problem-solving principles to data operations will be key to improving both efficiency and quality across our projects.

Your Impact

• Build, deploy, and maintain Python automation 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 Project Management team to ensure that data labeling meets accuracy standards and troubleshoot any issues related to data quality. 
• Plan quality assurance workflows to use GenAI and open-source models to find data anomalies.
• Set up monitoring tools to track the performance of data annotation operations, reporting key metrics and areas for improvement to leadership.
• Integrate and manage third-party api tools with Labelbox, ensuring seamless operation and data flow across platforms.
• Ability to build and maintain internal tools with retool and similar tools.
• Provide ongoing technical support to the project managers and labelers, assisting with technical challenges in Labelbox and associated tools.

What You Bring

• 3+ years of working experience in a technical role, interfacing with technical and non-technical teams, and writing Python scripts for data processing.
• 2+ years of experience using LLMs in prompting frameworks (e.g. LLM-as-a-judge).
• Some experience with machine learning models in scripts or data pipelines.
• Bachelor’s Degree in Engineering, Computer Science, or a technical field.
• Practical experience using LLMs or traditional models to assist annotation QA or generate/transform data.
• Proficiency in Python scripting and experience with automation of operational tasks.
• 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, data analysis, and data workflow management.
• Familiarity with cloud platforms such as AWS, GCP, or Azure.
• English fluency.
• Knowledge of Statistical