TL;DR

Machine Learning Engineer (AI): Owning and continuously improving Eneba's Featured Offers pricing algorithm with an accent on model design through experimentation to production monitoring. Focus on building and iterating on willingness-to-pay and price elasticity models using behavioural signals and contributing pricing-relevant features to the feature store.

Location: Remote

Salary: €55,000 - €70,000 a year

Company

Eneba is building an open, safe and sustainable marketplace for gamers, supporting close to 20m+ active users.

What you will do

  • Own and continuously improve Eneba's Featured Offers pricing algorithm — from model design through experimentation to production monitoring.
  • Build and iterate on willingness-to-pay and price elasticity models using behavioural signals: purchase history, browsing patterns, session data, price sensitivity indicators.
  • Collaborate with Product and Marketing/Growth to define pricing strategies for promotional campaigns and featured placements.
  • Define and track evaluation metrics connecting model output to business KPIs — revenue per session, conversion rate, margin, promotional ROI.
  • Work with Data Platform and Backend Engineering to ship pricing models as low-latency APIs integrated into live marketplace surfaces.
  • Monitor deployed models for data drift, distribution shifts, and degradation; own observability and alerting.
  • Contribute pricing-relevant features to the feature store — user price sensitivity signals, historical purchase behaviour, category-level demand indicators.

Requirements

  • Hands-on production experience building models that optimise pricing decisions — promotional pricing, demand-based pricing, or personalised pricing. You've shipped something that moved a revenue number.
  • Experience modelling willingness to pay, price elasticity, or conversion probability as a function of price. You're comfortable working with implicit signals and sparse, noisy data.
  • End-to-end ML ownership — you've taken models from raw data through feature engineering, training, evaluation, API 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.
  • Good English level is required, proficiency is preferred.

Nice to have

  • Experience with bandit algorithms or reinforcement learning for online pricing optimisation.
  • Familiarity with causal inference methods (uplift modelling, difference-in-differences) for pricing experiments.
  • Real-time or streaming inference experience (Kafka, Flink) for session-aware pricing.
  • Familiarity with Databricks and/or Apache Spark for large-scale data processing.
  • Production experience with feature stores (Databricks Feature Store, Hopsworks, Feast, or similar).
  • Background in marketplace economics, auction theory, or game-theoretic pricing.
  • Experience with setting up and evaluating A/B tests.
  • Strong business communication skills — you can translate model results and experimental findings into clear, actionable language for product and commercial stakeholders.

Culture & 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.

Hiring process

  • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses.
  • Final hiring decisions are ultimately made by humans.