Machine Learning Engineer, Marketplace

🕒 March 13

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Eneba

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

🛍️ eCommerce

🎮 Gaming

🏪 Marketplace

eCommerce • Gaming • Marketplace

Eneba is an online platform that offers a wide range of video games, gaming-related eCards, and gift cards. Users can easily purchase cheap games for various platforms, including PC and gaming consoles, and benefit from personalized game deals through a loyalty program. Eneba also provides an opportunity for businesses to sell their products on the platform, creating a marketplace for gamers and developers.

📋 Description

• 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.

🏖️ Benefits

• 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.