🆕 Lead ML Engineer (Broadcast Media) | București, CIM, 3/5 hibird

About the company Posted: May 12, 2026. You will join one of the most influential media organizations in the country — a market-leading broadcaster with a strong digital footprint and a nationwide audience.The company operates a complex broadcast and digital ecosystem, delivering live television, online streaming, and multimedia content to millions of viewers daily. Its technology teams support mission-critical infrastructure used in production, distribution, and digital media platforms. This is an environment where IT reliability, security, and performance directly impact live broadcasting operations and large-scale content delivery — making technology a core pillar of the business, not just a support function.You will be part of a stable, high-impact organization that values innovation, security, and operational excellence in a fast-paced media landscape. About job Build and scale recommendation & personalization systems for a large-scale streaming platform. Work on retrieval, ranking and reranking architectures serving millions of users. Contribute to the evolution of an existing production recommendation system . Collaborate closely with senior Data Science and ML Engineering experts. Help shape the future of content discovery and audience intelligence. Responsibilities Develop Deep Learning models and personalization algorithms using Python , PyTorch or TensorFlow . Drive A/B testing and experimentation frameworks to improve product impact. Build solutions for recommendation challenges such as cold-start , shared accounts and mixed user intent. Deliver scalable and production-ready components within the ML pipeline . Improve methodologies related to audience behavior forecasting and content intelligence. Requirements Requirements Strong experience building and deploying large-scale recommendation systems . Deep understanding of Deep Learning , embeddings and representation learning. Experience with ML deployment , monitoring and debugging in production environments. Strong knowledge of A/B testing , evaluation metrics and statistical significance. Ability to work with massive-scale behavioral datasets and complex recommendation challenges. Nice to have Experience with Graph Neural Networks (GNNs) such as LightGCN or GraphSAGE. Familiarity with online learning and multi-armed bandit approaches. Exposure to cloud ecosystems and modern MLOps workflows . Previous experience within streaming , media or entertainment platforms . Understanding of recommendation edge cases such as popularity bias and oversmoothing. Benefits Medical allowance. Holiday vouchers. Employee discounts. Office fruit & on-site massage. Employee Assistance Program.