Machine Learning Engineer, Marketplace

Eneba is building a sustainable marketplace for gamers, supporting over 20 million active users. They are seeking a Machine Learning Engineer to enhance their recommendation systems, taking ownership of the full ML lifecycle to improve user engagement and revenue.

Responsibilities:

• Analyse user behaviour data (purchase history, browsing patterns, game genre preferences, session signals) to identify high-value personalisation features
• Design, train, and iterate on recommendation models — from collaborative filtering and matrix factorisation to sequence-based and embedding-based approaches
• Build and maintain end-to-end training and serving pipelines in collaboration with data and backend engineers
• Define and track evaluation metrics — offline (precision@k, NDCG, coverage) and online (CTR, conversion, revenue per session) — tied directly to business KPIs
• Run rigorous A/B tests to benchmark new approaches against the current internal baseline
• Own monitoring and observability of deployed models: data drift, prediction distribution shifts, latency, degradation
• Contribute reusable user and item features to our feature store

Requirements:

• Hands-on experience designing and shipping recommender systems — collaborative filtering, content-based, hybrid, or sequence-based. You've gone beyond tutorials and built things that shipped and improved real metrics
• End-to-end ML ownership — you've taken models from raw data through feature engineering, training, evaluation, API wrapping, deployment, and production monitoring. You don't hand off at the notebook stage
• Strong Python and MLOps fluency — extensive Python for model development, plus experience with MLOps tooling (MLflow or similar) for experiment tracking, model versioning, and lifecycle management
• Experience with real-time or streaming inference (Kafka, Flink) for session-based recommendations
• Familiarity with Databricks and/or Apache Spark for large-scale data processing
• Production experience with feature stores (Databricks Feature Store, Hopsworks, Feast, or similar)
• Knowledge of two-tower / embedding-based retrieval at scale
• Familiarity with bandit algorithms or reinforcement learning for online recommendation optimisation
• Strong business communication skills — you can translate model results and experimental findings into clear, actionable language for product and commercial stakeholders
• Good English level is required, proficiency is preferred