Machine Learning Engineer
At a glance
Mid-level Machine Learning Engineering role at Easybrain. Limassol.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build real-time services for ad pricing, ad load and in-game choices such as difficulty or content
- Create pipelines and fast queries that process large volumes of behavioral data
- Take models from research into production with data scientists, defining needs and interfaces while owning implementation
- Set up deployment workflows, monitoring and checks on model quality
What you bring
- You have built and operated Python services in production, including improving their performance
- You bring strong SQL skills and practical experience working with large datasets
- You have worked in data engineering and MLOps, including pipelines, deployment automation and monitoring
- You can discuss machine learning and A/B testing with data scientists, and fluent Russian is required
Who this fits
This mid-level role suits an engineer who can bridge backend development, data engineering and production ML work. It is based in Limassol, Cyprus, with a hybrid arrangement.
From the employer
Easybrain is looking for a Machine Learning Engineer to join our Business Intelligence team. Our ML models run in production, powering LTV prediction and user acquisition optimization across a portfolio of games with billions of installs. We are now building new ML systems for ads monetization and game personalization, and many of them make decisions in real time.
You will work alongside Data Scientists as an equal partner and own the engineering side of these systems: real-time services, data pipelines, and deployment and monitoring infrastructure. The role combines backend development, data engineering and MLOps, with a lot of work on large-scale data.
Responsibilities:
- Designing and building real-time ML services for ads monetization (floor pricing, ad load) and in-game personalization (difficulty, content);
- Building data pipelines and performant queries over large-scale behavioral data;
- Taking models from research to production together with DS: shaping requirements, agreeing on interfaces, and owning the production part;
- Setting up CI/CD, monitoring and model quality tracking.
Tech stack: Python, ClickHouse, PostgreSQL, Airflow, MLflow, Docker; PyTorch and LightGBM models in production.
Requirements:
- Experience building and running production services in Python, including performance optimization;
- Strong SQL and hands-on experience with large data volumes;
- Experience in data engineering and MLOps (pipelines, CI/CD, monitoring);
- Understanding of ML and A/B testing well enough to work on equal terms with DS;
- Ability to understand business context, gather requirements and own technical decisions;
- Experience with high-load or low-latency systems, ad tech, or mobile games is a plus;
- B2+ level of English;
- Fluent Russian is required.
Benefits:
Besides the engaging tasks, support from experienced colleagues, challenge, and drive, we offer:
- Full support in relocating to countries where our offices are located;
- High-end market salary with performance bonuses;
- All needed equipment;
- Regular company events and monthly Friday meetings;
- Social benefits (private medical cover, sports reimbursement, etc.);
- Paid vacations, sick days;
- English, Greek, and Polish online language classes;
- Reimbursement for education and professional development.