Eneba
Machine Learning Engineer, Dynamic Pricing & Optimisation
EnebaLithuania5 hours ago
Full-timeRemote FriendlyEngineering, Information Technology
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.

About The Team

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 Problem You'll Own

Pricing is one of the most direct levers on revenue in a marketplace. At Eneba, we've built internal pricing algorithms that power our Featured Offers — and we want someone to take full ownership of that system and push it further.

You'll own the algorithm end-to-end: understanding how users respond to price, modelling willingness to pay at the individual level, running experiments, and shipping improvements that show up directly in revenue. You'll work at the intersection of ML, economics, and product — and the impact of your work will be measurable from day one.

Responsibilities

  • 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.

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.

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.

  • Salary ranges may vary. We’re seeking candidates with varied experience levels; from individual contributors to functional leaders in this space.
  • We’re an international team and our business language of choice is English. Good English level is required, proficiency is preferred.

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