Principal Data Engineer, VideoAmp

• 7+ years of experience in Machine Learning Engineering, Data Engineering, or a similar technical role
• Expert-level proficiency in Python, ML frameworks, and distributed data processing (Spark, Hive)
• Deep understanding of data quality methodologies, fault-tolerant data systems, and validation frameworks
• Strong experience designing and scaling ML infrastructure, automated pipelines, and production-grade deployment workflows
• Hands-on experience with cloud-native architectures, ideally on AWS
• Expertise with CI/CD, version control, and modern DevOps practices
• Strong communicator with the ability to translate complex technical concepts into clear, actionable insights
• Demonstrated ability to lead cross-functional technical initiatives with minimal guidance
• Above all, if you’re eager to influence and support the revolutionary goals of a dynamic, cross-functional team—join us and make an impact!

Desirable

• As a Principal Machine Learning Engineer, you will play a critical role in shaping the next generation of VideoAmp’s data and measurement products
• You will architect large-scale ML systems, elevate data quality across the organization, and lead complex initiatives that improve the accuracy, reliability, and performance of our datasets and ML pipelines
• This role requires deep technical expertise, strong cross-functional influence, and the ability to drive alignment across engineering, data science, and product teams
• Architect and lead development of advanced machine learning models, quality frameworks, and large-scale data validation systems that power VideoAmp’s measurement and optimization products
• Design and optimize ML infrastructure, including scalable distributed pipelines, model lifecycle tooling, and automated validation frameworks
• Lead experimentation strategies, including model benchmarking, reproducibility, evaluation methodologies, and statistical validation
• Drive data quality standards across the organization by partnering with Data Engineering, Core Engineering, and Measurement Science teams
• Own cross-functional initiatives end-to-end, from requirements definition through production deployment, monitoring, and iteration
• Influence technical direction, contributing to architectural decisions, design reviews, and long-term platform strategy
• Mentor and uplevel engineers, providing guidance on ML best practices, system design, data quality, and code excellence
• Communicate complex findings, insights, and recommendations to technical and non-technical stakeholders