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.
Our Data team sits at the core of that growth. We build the ML systems, data pipelines, and platform capabilities that drive intelligent decisions across the business — from fraud and risk to personalisation and LTV. If you want your models to matter, this is the place.
The Product Growth team this role attaches to is similarly oriented: We build technical solutions to enable more and better marketplace activity for our customers.
The Problem You'll Own
Recommendations are one of the highest-leverage surfaces on our marketplace. We already have a recommendation system in production — and now we want someone to take us to the next level.
This isn't a "maintain and monitor" role. We're looking for an engineer who will challenge our current approach, prototype new ideas, run experiments, and ship models that measurably move engagement and revenue. You'll own the full recommendation ML lifecycle — from understanding user behaviour signals to deploying and iterating on production-grade models — and work closely with product, engineering, and data platform teams to make it happen.
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
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.
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
Founded by two college friends and avid gamers Vytis and Zygis, Eneba was launched in 2018. We’re a fast-growing company from Europe with a team of over 200 e-commerce and gaming experts worldwide, trusted by over 10 million registered users.
For gamers: a one-stop shop for all your gaming and entertainment needs with the best deals on over 80,000 products, secure payments, quick refunds, and ready-to-help customer support.
For game publishers: an established and trusted alternative distribution platform that opens up new markets and revenue streams, and enables you to lower operational costs.
For sellers: a fair playing field to present your offer to gamers, compete with other verified sellers, and sell your stock using our simple tools and automated API integration.
For creators and partners: an affiliate program with unlimited earnings, no minimum withdrawals, 30-day tracking cookies, optimization tools, and hands-on support.
For future team members: flexible on-site, hybrid, and remote career opportunities to fuel your personal and professional growth.
Together, we're on a mission to enable everyone to discover the joy of gaming!
