Machine Learning Engineer, Marketplace

About Eneba

At Eneba, we’re building an open, safe and sustainable marketplace for the gamers of today and tomorrow. Our marketplace supports close to 20m+ active users (and growing fast!), provides a level of trust, safety and market accessibility unparalleled to none. We’re proud of what we’ve accomplished in such a short time and look forward to sharing this journey with you. Join us as we continue to scale, diversify our portfolio, and grow with the evolving community of gamers.

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.

Nice to have

• 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.

What It’s Like To Work At Eneba

• Opportunity to join our Employee Stock Options program.
• Opportunity to help scale a unique product.
• Various bonus systems: performance-based, referral, additional paid leave, personal learning budget.
• Paid volunteering opportunities.
• Work location of your choice: office, remote, opportunity to work and travel.
• Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes.
• Please attach CV's in English.
• To find out about how we handle your personal data, make sure to check out our Candidate Privacy Notice https://www.eneba.com/candidate-privacy-notice

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.