Job Description

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.

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