Machine Learning Engineer
Location
Belgium + 5 moreAll locations: Belgium | Germany | Italy | Netherlands | Spain | United Kingdom
Posted
43 days ago
Salary
0
Seniority
Mid Level
No structured requirement data.
Job Description
Machine Learning Engineer
Seedtag
We’re 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 - 2–4 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, 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’ll ensure a positive experience. Send us your CV and let’s build the future of neuro-contextual advertising together.
Related Guides
Related Job Pages
More Machine Learning Engineer Jobs
• Implementation of production ML infrastructure: Design, implement, and maintain machine learning pipelines and systems in production environments, ensuring scalability, reliability, monitoring, and integration with the platform architecture • Development and evolution of product-focused models: Develop, train, validate, and optimize ML models with an emphasis on direct impact to product features and value generation for customers • MLOps and model governance: Establish practices for model versioning, deployment, monitoring, auditing, and traceability, ensuring reproducibility, security, and compliance across the solution lifecycle • Monitoring and performance of ML systems: Track performance, accuracy, drift, and model stability metrics in production, proposing continuous adjustments to ensure operational efficiency and sustainability • Integration with engineering architecture: Collaborate with Software Engineering and Data Engineering to ensure ML systems align with architectural standards, coding best practices, and scalability requirements • Code quality and sustainability: Produce clean, testable, and efficient code, considering time complexity, computational cost, and long-term maintainability, following mature engineering standards • Applied research and product-driven innovation: Explore new techniques, frameworks, and ML approaches with a focus on practical applicability and concrete improvements to product and infrastructure • Cross-functional collaboration and technical support: Act as a technical ML reference within Engineering, supporting Product and other areas in understanding intelligent systems and ensuring alignment between strategy and technical execution
• Definir e evoluir a arquitetura de Machine Learning e aplicações com IA generativa (LLMs, agentes, RAG), estabelecendo direções técnicas de longo prazo com foco em escalabilidade, confiabilidade e impacto no produto. • Estabelecer padrões de excelência para modelagem, versionamento, validação e monitoramento, além de diretrizes para uso de IA generativa (qualidade, custo, observabilidade, segurança e uso responsável). • Guiar a evolução de MLOps, garantindo pipelines robustos, deploy contínuo, monitoramento e governança de modelos e sistemas de IA em produção. • Identificar e liderar iniciativas estratégicas de ML e IA generativa, direcionando aplicações de alto impacto no produto e no negócio. • Atuar nos problemas mais críticos da companhia, definindo soluções e trade-offs de longo prazo que equilibrem escala, custo e complexidade. • Influenciar decisões técnicas entre times de Engenharia, Dados e Produto, garantindo consistência arquitetural e visão de longo prazo. • Elevar o nível da equipe por meio de mentoria, revisão de soluções e disseminação de boas práticas, fortalecendo a maturidade da disciplina.
Machine Learning Scientist 5 – Games
NetflixDescribed as the world's top internet television network, Netflix is a publicly-traded entertainment company offering video-on-demand and streaming media. As an
• Develop sophisticated embeddings and models that incorporate deep game-specific signals to solve high-impact business problems, including audience insights, opportunity sizing, and forecasting. • Build the tools, models, and pipelines required to accelerate DSE workflows across games portfolio, studios, product, and platform. • Act as a key liaison with the broader Netflix DSE and AI teams to adopt, adapt, and tailor global Netflix capabilities for the unique requirements of the gaming space. • Create end-to-end ML pipelines that accelerate and enable DSE members across games to uncover actionable insights and build data-intensive game features. • Establish the technical standards for how ML capabilities are applied across game domains.
Staff Machine Learning Engineer – GenAI, Automation
Redwood LogisticsRedwood Logistics is a modern 4PL provider that orchestrates logistics execution and supply chain technology.
• Develop and implement automation solutions that leverage statistics, machine learning, and Gen AI technologies within Redwood, impacting key metrics and streamlining network operations. • Identify new opportunities to drive business impact through data exploration and collaboration with key stakeholders. • Develop and optimize our agentic ML framework for speed, reliability, and observability. • Collaborate with cross-functional teams, including software engineers, data scientists, business stakeholders, and product teams, to gather inputs, assumptions, and understand business processes along the way.
