UK remote
MLops Engineer
About this role
We’re looking for an MLOps Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale. Who We Are At Seedtag, our mission is to transform advertising by proving that effectiveness and user privacy can truly coexist. As the leading Neuro-Contextual Advertising Company, we combine Artificial Intelligence, Natural Language Processing, Computer Vision, and neuroscience to understand not only what content is about, but how it makes people feel and what they intend to do next.
Our proprietary AI, Liz, enables brands to connect with audiences across the open web and Connected TV without cookies or user tracking. Founded in 2014 by two ex-Googlers, Seedtag has grown to 500+ Seedtaggers in 17 countries, backed by €250M in funding, and operates today as a global ad-tech leader. If you enjoy solving complex engineering challenges and building AI-driven systems at scale, you’ll feel right at home here.
Your Challenge As our MLOps Engineer, you will help scale and maintain the infrastructure behind Liz, Seedtag's contextual AI, ensuring reliable, efficient, and low-latency ML model deployment across the open web and CTV. Your main responsibilities will include: ML Model Deployment: Deploy, maintain, and optimise ML models in production, ensuring performance, reliability, and latency standards. Data Pipelines: Build and improve pipelines for dataset generation, model training, validation, and performance analysis.
Model Observability: Implement monitoring, dashboards, drift detection, and alerting to ensure model quality and stability. MLOps Infrastructure: Improve CI/CD pipelines, model versioning, reproducibility, and infrastructure automation. Performance & Cost Optimisation: Identify opportunities to reduce serving costs and improve efficiency without compromising model quality. AI Automation: Develop internal automations and agentic integrations to streamline ML workflows.
Cross-Functional Collaboration: Work closely with Data, Platform, and Backend teams to troubleshoot production issues and improve ML systems. Our Core Values Outcome over Output We measure success by impact and value, not by volume of features or lines of code. Failure Is Allowed, Learning Is a Must Experimentation is key to innovation. We test early, iterate often, and learn fast. We Are All Scouts We take ownership and leave things better than we found them.