Machine Learning Engineer (Recommendations)

As a Data Scientist (ML Engineering) on the Product Engagement team, your mission is twofold: keep our recommendation infrastructure robust, scalable, and production-ready, and explore and validate more advanced recommendation algorithms that could take our personalization to the next level. Where the Data Scientist (Recommendations & Experimentation) designs the statistical logic, you are the person who makes sure it actually works in production - at scale, reliably, and fast - while also pushing the frontier of what our recommendation engine is capable of technically.

What you'll do

• Own the production recommendation infrastructure: maintain and improve the systems that serve personalized content to millions of users, ensuring reliability, low latency, and scalability as the catalog and user base grow.

• Research and prototype advanced recommendation algorithms: explore newer approaches - deep learning-based models, contextual bandits, session-based recommendations, graph-based methods - evaluate their potential, and run controlled experiments to validate uplift before production.

• Produce ML models and pipelines: take prototypes (from yourself or from the team's Data Scientist) and turn them into production-grade, monitored, maintainable features integrated into the live recommendation engine.

• Design scalable infrastructure: anticipate bottlenecks and design systems that can handle larger catalogs, more complex segmentations, and higher traffic - including serving layer optimization, caching strategies, and pipeline orchestration.

• Build and maintain data pipelines in DBT and Databricks, ensuring clean transformations, data quality, and robust experimentation frameworks that the team can rely on.

• Monitor model health in production: define retraining strategies, detect drift, and ensure recommendation quality is measured and maintained over time.

• Collaborate closely with the Data Scientist and Senior Analyst to translate statistical insights and business requirements into engineering decisions.

What you'll bring

• Python for ML and infrastructure: strong Python skills applied to model training, evaluation, deployment, and pipeline scripting. Writes production-quality, testable, version-controlled code - not just notebooks.

• SQL and DBT: solid SQL and hands-on DBT experience to build and maintain reliable transformation pipelines with clear data lineage and quality controls.

• ML production on AWS: hands-on experience deploying and monitoring ML models using AWS services (SageMaker, Lambda, ECS, Step Functions). Understands model drift, monitoring strategies, and retraining triggers.

• Batch ML model training and evaluation pipelines: design, build, and maintain scalable machine learning training and evaluation pipelines that support recommendation systems and related personalization use cases. This includes developing robust, well-monitored workflows for model development, deployment, and continuous improvement, while contributing to the evolution of the recommendation infrastructure toward more adaptive and responsive systems over time.

• Advanced ML algorithms: familiarity with recommendation techniques beyond collaborative filtering - e.g. neural approaches (two-tower models, transformers for sequences), contextual bandits, learning-to-rank. Knows how to evaluate and compare them rigorously.

• Orchestration and CI/CD: experience with orchestration tools (Airflow, Prefect, or Dagster) for reliable, observable pipelines, and comfort with Git and CI/CD workflows for ML systems.

• Scalability and system design mindset: can anticipate infrastructure bottlenecks, reason through architecture trade-offs (batch vs. streaming, horizontal vs. vertical scaling), and connect engineering decisions to business outcomes.

Nice to have

• Experience with real-time or low-latency serving layers (Redis, DynamoDB or equivalent) - the system is currently batch, but session-level adaptation is a future direction.

• Experience with experimentation frameworks for ML systems, including online evaluation of recommendation algorithms (A/B tests, interleaving, counterfactual evaluation).

• Knowledge of modern data stack tools (Snowflake, BigQuery, Fivetran).

• Exposure to knowledge graph or content graph approaches for content-aware recommendation.

• Interest in balancing data-driven optimization with pedagogical or brand-driven constraints (e.g. content diversity goals, curated onboarding, character injection).

• English is a must: We’re a multicultural team providing a service in English, so while certifications aren’t necessary, fluency is essential. As a fully remote company, clear and effective spoken and written communication, especially in asynchronous and long-form formats, is key to collaborating successfully.

Conditions:

• Remote-Friendly: Work from where you’re most productive, home or our offices in Madrid, anywhere within a 2-hour difference from Spain (GMT+1). The choice is yours!

• Visa Sponsorship: If you need a visa to work in the EU, we’ll handle the process and cover the costs to make your transition seamless.