Machine Learning Engineer
Seedtag · Spain
- Hybrid
- Full-time
- $110,000 / year
- Spain
Job highlights
- Build AI for ad revenue optimization.
- Deploy and maintain AI models online.
- Design ML algorithms and control systems.
- Develop end-to-end data pipelines.
- Improve MLOps infrastructure and workflows.
About the role
Machine Learning Engineer
We e looking for a Machine Learning 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 700+ 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 a Machine Learning Engineer on Seedtag's Ad Exchange team, you will:
- Build cutting-edge AI to optimise revenue flow while ensuring the needs of publishers and advertisers are met.
- Research, design, test, deploy, and maintain AI models in a fully online environment to maximise margins, reduce operational costs, and enhance Seedtag's targeting capabilities.
- Design and implement classical ML algorithms and control systems to ensure delivery of internal campaigns and maximise monetisation outcomes.
- Build end-to-end data pipelines to train, validate, and analyse production model behaviour through custom dashboards.
- Continuously improve our MLOps infrastructure, CI/CD pipelines, internal automations, and AI-supported workflows.
- Collaborate closely with Data, Platform, and Backend Engineers to build services and infrastructure, from dataset generation to live model validation.
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.
- We Are Data-Driven: Data informs our decisions and helps us continuously improve our systems and results.
Tech Stack
We operate at a large scale, supporting up to 120k requests per second, with ML models responding in under 10 milliseconds and processing 20 TB of data daily.
Our Stack Includes:
- Python & Go microservices
- Kafka, Kinesis, Redis, GCS
- Kubernetes on GCP & AWS
- Druid, MongoDB, scalable data lake architecture
- Typescript (Node.js) and Scala across other parts of the company
What You ll Need to Succeed
- 3–6 years of experience building and deploying ML systems in production.
- Strong Python skills and solid software engineering fundamentals (APIs, async programming, testing, clean architecture).
- Experience working on both model development and production deployment.
- Understanding of distributed systems, microservices, and cloud-native environments.
- Familiarity with MLOps practices: model versioning, monitoring, CI/CD, reproducibility.
- Experience with NLP, embeddings, and/or ranking models is a plus.
- Comfortable debugging across layers: model behaviour, data issues, API performance, infrastructure bottlenecks.
- Strong ownership mindset and ability to operate autonomously in fast-moving environments.
Why Join Seedtag?
- A key moment of growth with real ownership and global impact.
- Flexible work model with 100% remote or hybrid options. (Remote contracts available in Spain, Italy, the UK, Belgium, the Netherlands, France, and Germany.)
- Continuous learning through a learning platform and optional language classes.
- A supportive, trust-based culture that values well-being.
- Team activities, offsites, and opportunities to connect beyond work.
Additional Perks
- Home office setup budget up to €1,000
- Paid trips to our HQ in Madrid
- MacBook Pro M3
Ready to Join the Seedtag Adventure?
At Seedtag, we create an environment where everyone can thrive. If you need accommodations during the hiring process, let us know, and we cll ensure a positive experience.
Send us your CV and let s build the future of neuro-contextual advertising together.
Key skills/competency
- Machine Learning
- Python
- MLOps
- Data Pipelines
- AI Models
- Software Engineering
- Cloud-native Environments
- NLP
- Distributed Systems
- Microservices
Skills & topics
- Machine Learning Engineer
- AI
- MLOps
- Python
- Data Pipelines
- AdTech
- Neuro-Contextual Advertising
- NLP
- Distributed Systems
- Cloud Engineering
How to get hired
- Tailor your resume: Highlight experience with ML systems, Python, and MLOps.
- Showcase production experience: Emphasize deploying and maintaining AI models.
- Demonstrate understanding: Discuss distributed systems and cloud environments.
- Highlight ownership: Mention autonomous work in fast-paced settings.
- Prepare for interviews: Be ready to debug across layers and discuss AI impact.
Technical preparation
Practice Python for ML and software engineering.,Study distributed systems and microservices.,Familiarize with MLOps tools and practices.,Prepare to debug complex systems.
Behavioral questions
Describe a complex ML system you deployed.,How do you handle learning from failure?,Give an example of taking ownership.,How do you use data to drive decisions?
Frequently asked questions
- What is the typical career progression for a Machine Learning Engineer at Seedtag?
- At Seedtag, Machine Learning Engineers can progress through various levels of technical expertise and leadership. With 3-6 years of experience, you'll start by building and deploying ML systems. As you grow, you can take on more complex challenges, lead projects, or specialize in areas like NLP or MLOps. Seedtag's growth environment and focus on ownership provide opportunities for rapid advancement based on impact and learning.
- What kind of AI models does Seedtag build for neuro-contextual advertising?
- Seedtag builds sophisticated AI models that go beyond content understanding to gauge user emotion and intent. This includes models for NLP, computer vision, and neuroscience applications. The Machine Learning Engineer role involves researching, designing, testing, deploying, and maintaining these models to optimize ad targeting, revenue, and user experience without relying on traditional tracking methods.
- How does Seedtag ensure user privacy while using AI for advertising?
- Seedtag's core mission is to prove that effectiveness and user privacy can coexist. Their AI, Liz, operates within a neuro-contextual framework, understanding content and audience sentiment without cookies or user tracking. This approach ensures that advertising is contextually relevant and privacy-compliant by design.
- What are the expectations for a Machine Learning Engineer regarding MLOps at Seedtag?
- The Machine Learning Engineer is expected to continuously improve Seedtag's MLOps infrastructure. This includes aspects like model versioning, monitoring, CI/CD pipelines, internal automations, and AI-supported workflows. A familiarity with these MLOps practices is crucial for ensuring the efficient and reliable deployment and maintenance of AI models in a production environment.
- What is the work environment like for a Machine Learning Engineer at Seedtag?
- Seedtag fosters a supportive, trust-based culture focused on well-being and continuous learning. The work model is flexible, offering 100% remote or hybrid options in several European countries. Engineers are encouraged to take ownership, experiment, and learn from failures, contributing to a dynamic and innovative environment.