Machine Learning Data Engineer — Computer Vision & Datasets

We're looking for a Machine Learning Data Engineer to build and maintain the data pipelines and infrastructure that power our computer vision and machine learning initiatives.

In this role, you'll manage large-scale visual datasets, automate data preparation workflows, and ensure our AI engineers have access to reliable, well-structured, and high-quality training data. You'll work closely with AI engineers, annotation specialists, and infrastructure teams to improve the efficiency, quality, and reproducibility of our ML development process.

The ideal candidate has strong data engineering fundamentals along with practical experience handling image datasets, data annotation workflows, and machine learning data preparation.

Key Responsibilities

• Dataset Engineering: Build and maintain scalable pipelines for collecting, processing, organizing, and preparing image datasets for training, validation, and evaluation.
• Data Quality & Validation: Develop automated checks for corrupted images, duplicates, missing metadata, inconsistent labels, and class imbalance.
• Data Versioning & Traceability: Implement dataset versioning, lineage tracking, metadata management, and reproducible data preparation workflows.
• Annotation Workflows: Integrate annotation tools with storage systems and ML pipelines. Automate label import, export, validation, and review processes.
• Data Infrastructure & Automation: Develop ETL/ELT pipelines and automated processing jobs using appropriate orchestration and distributed processing tools.
• Performance & Reliability: Optimize data processing speed, storage efficiency, and infrastructure costs. Implement monitoring, logging, error handling, and recovery mechanisms.
• Cross-functional Collaboration: Work with ML engineers to translate model training requirements into reliable data workflows and maintain clear documentation of datasets and transformations.

Required Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or a related field, or equivalent practical experience.
• 3+ years of professional experience in data engineering, data infrastructure, or related engineering roles.
• Strong proficiency in Python and SQL.
• Experience building and maintaining ETL/ELT pipelines and automated data processing workflows.
• Experience working with large-scale datasets, cloud storage, databases, and APIs.
• Understanding of data validation, cleaning, transformation, and data integrity principles.
• Familiarity with image data, computer vision datasets, and common dataset formats.