Limassol, Lemesos, Cyprus

Mayflower is a technology company that alters the entertainment industry to a new level of perception and engagement.

We are building the future of live entertainment. We believe that by providing people with the best and highest-quality live streaming experience in entertainment, we can tackle the world’s “problems” better, together!

We are seeking an experienced ML Engineer (RecSys) to join our dynamic team ML team.

Our technology stack:

Languages: Python;
Monitoring: Prometheus, VictoriaMetrics, Grafana, Grafana Loki, Sentry;
DBs: MariaDB, Clickhouse, Redis;
Streaming: Apache Kafka;
Workflow management: Apache Airflow, Argo Workflow;
Infrastructure: k8s, gitlab.

Job Responsibilities

Develop and improve offline and online recommendation systems for live video streams using both classical ML and deep learning approaches.
Build and optimize ranking and retrieval models that surface relevant content to users.
Design and implement personalized discovery systems — including context-aware feeds, donation menu recommendations, new recommendation scenarios and content layouts that continuously enhance the personalized user experience across the platform.
Deploy ML solutions into production, integrating them with backend systems and ensuring performance, scalability, and observability.
Set up and maintain monitoring, retraining, and data pipelines for deployed models.
Evaluate recommendation quality and collaborate closely with the analytics team to design and interpret A/B tests that measure model impact.

Job requirements

Degree in Computer Science, Mathematics, or a related technical field.
3+ years of experience in Data Science / Machine Learning.
1+ year of experience building recommendation systems in production.
Proven experience implementing and deploying ML models to production environments.
Strong understanding of machine learning algorithms, ranking/retrieval pipelines, and personalization architectures.
Ability to research, prototype, and reproduce modern ML methods from academic papers.
Proficiency in Python 3 and hands-on experience writing production-quality code.

Qualification that can be a plus:

Experience working with video and image content in the context of ML (e.g., visual embeddings, content similarity, multimodal models).
Experience with large-scale distributed training and data-intensive ML pipelines.

Conditions

We know that great talent deserves great conditions, so here's what you can expect when joining us:

EU-based employment contract and a 3-year Cyprus work visa with full support for your relocation and visa processes, including assistance for your family.
Full relocation package: flights to Limassol for you and your family, a company-covered apartment for the first month, and full relocation support to make your move smooth and hassle-free.
Transparent performance reviews twice a year, with bonus opportunities and salary adjustments.
Private medical insurance for you and your family, a corporate mobile plan (unlimited in Cyprus with roaming included), and interest-free support for car purchases.
Mindfulness & well-being support, including psychological assistance with 50% coverage.
50% coverage of school and kindergarten fees for your children.
Fully covered sports benefits, and also access to in-house electric scooters and bike rentals, and cycling purchase compensation.
Investment in your growth: paid language courses and access to suited-for-you development programs, including conferences, training programs, and coaching to support your professional journey.
A culture of recognition: a peer reward program to celebrate your contributions.
A fully equipped office in Limassol’s city center, with everything you need for deep work and collaboration.
Free catering in the office and an in-house coffee bar with high-quality drinks and a health bar stocked with nutritious snacks.
A strong engineering culture: international teams, corporate events, team buildings, and hackathons—because great work happens in great communities.

Recruitment process

HR interview (40 min);
Interview with watcher (1.5 hour);
Live-coding (1.5 hour);
Final interview. (1 hour).

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