Machine Learning Engineer - Web Data Quality

AI overview

Design and implement intelligent systems for quality improvement of large-scale web datasets, employing modern AI techniques and collaborating with cross-functional teams.

At Zyte, we make the world’s web data accessible to everyone. Our technology powers data extraction at scale, helping businesses and researchers unlock the full potential of the web.

We’re a remote-first, multicultural team of engineers, data scientists, and innovators who believe in curiosity, collaboration, and continuous learning. If you’re passionate about building reliable AI systems and improving the quality of web data, we’d love to hear from you.

About the Role

As a Machine Learning Engineer (Web Data Quality), you’ll design and implement intelligent systems that automatically detect, measure, and improve the quality of large-scale web datasets. You’ll work at the intersection of data science, AI, and distributed systems, collaborating closely with product, engineering, and data teams to make data accuracy measurable, scalable, and actionable.

Requirements

What You’ll Do

Develop and deploy ML models for anomaly detection, schema drift, and content validation
Build and improve data quality pipelines leveraging modern data and MLOps tools
Design and optimize embeddings and GenAI models to enhance data consistency
Collaborate with engineers to integrate AI systems into production workflows
Conduct experiments, evaluate performance, and iterate for continuous improvement
Stay up to date on AI/ML and GenAI research to guide innovation within Zyte

Required

3+ years of experience in Machine Learning / Data Science / AI Engineering
Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
Experience with data validation, anomaly detection, or data quality systems
Familiarity with data pipelines (Airflow, Spark, or similar)
Understanding of model evaluation, metrics, and deployment best practices
Excellent problem-solving, communication, and collaboration skills

Preferred

Experience with LangChain, LlamaIndex, or GenAI model orchestration
Familiarity with data labeling tools and active learning approaches
Contributions to open-source or public ML projects
Experience working in a remote, cross-functional team environment

Benefits

35 days of paid time off
Health & wellness support
Inclusive and supportive team environment
Attend conferences and meet with team members from across the globe.
Work with cutting-edge open source technologies and tools

Perks & Benefits Extracted with AI

Health Insurance: Health & wellness support
Paid Time Off: 35 days of paid time off