About the job
At CreatorIQ, we are the driving force behind creator-led growth, serving over 1,300 esteemed brands and agencies worldwide. Our mission is to infuse humanity into businesses while amplifying individual impact. Our core values guide us: be intentional, pursue excellence daily, embrace the journey collectively, and exemplify kindness. Recognized as one of the best companies to work for by BuiltIn LA and NY, CreatorIQ has also been featured as a Fastest-Growing Company in North America on the Deloitte Technology Fast 500™ for four consecutive years. In 2025, we were named a leader in IDC MarketScape: Worldwide Influencer Marketing Platforms for Large Enterprises, and recognized by The Forrester New Wave™: Influencer Marketing Solutions. Our innovative culture has earned us top ratings on G2 and a 5-star rating on Influencer MarketingHub. We believe in a flexible work model that balances in-person collaboration with remote adaptability, fostering an environment where innovation thrives.
Join us in our quest to revolutionize the industry with your passion and creativity!
Senior MLOps Engineer (Applied AI Focus)
As a Senior MLOps Engineer within our Product Innovations team, you will act as the technical authority for Applied MLOps, seamlessly integrating experimental data science with production-grade efficiency. Your primary focus will be on ground truth generation, model evaluation, and the intricacies of pre/post-processing within a scaled vector embeddings ecosystem.
In this role, you will have the opportunity to:
Design Annotation & Measurement Pipelines: Lead the creation and execution of human-in-the-loop and auto-annotation workflows. You will establish systems (e.g., Label Studio) to ensure high confidence metrics and rigorous Inter-Annotator Agreement (IAA).
Enhance Cost-Efficiency through Evaluation: Develop ground truth datasets, golden sets, and by-model evaluation criteria to benchmark models, enabling informed data science decisions. Your contributions will directly influence margin by ensuring the right model is deployed for the appropriate task.
Uphold Applied MLOps Standards: Implement
