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Praha, Česko
Hybrid
Join us and build a streaming platform used by millions.
At Sky Czech Republic, we’re building the tech backbone that powers some of the world’s biggest streaming services. Ever heard of Peacock in the U.S., or Sky Showtime in the Czech Republic? They all run on our global streaming platform—a kind of technological skeleton where each service plugs in its own content and branding. Our platform serves millions of users worldwide. Just to give you an idea—Peacock alone has 40 million users in the U.S.
Thousands of engineers globally are shaping this platform, and our Prague tech hub is a key part of that effort. But we don’t just keep the engine running—we push the tech boundaries of what’s possible, alongside teams from Lisbon, London, and New York. Here in Prague, we have teams specializing in frontend development (including mobile, TV, and web), backend development (Java), DevOps & Platform Engineering, AWS, and data science.
What is the plot?
We are working to advance our personalised recommendation systems by developing efficient, low-latency solutions that serve millions of users globally.
What role will you play?
As a Machine Learning Engineer, you will collaborate closely with data scientists, engineers, and product managers to design intelligent content recommendation mechanisms and drive the ongoing advancement of our Machine Learning Platform.
Your daily tasks:
ML Pipeline Engineering: Design, build, and maintain production-grade ML training pipelines using orchestration frameworks (TFX, Kubeflow Pipelines SDK, Airflow), handling the full lifecycle from feature engineering through to model testing, validation, evaluation and promotion.
Model Development: Train and optimise ML models for user personalisation — recommendation engines, ranking algorithms, user segmentation, and content analysis — at significant production scale.
Model Serving: Deploy and operate ML models via dedicated serving infrastructure (e.g. TensorFlow Serving, Triton, TorchServe), ensuring low latency, high availability, and continued performance in production.
Monitoring & Optimisation: Track model performance and quality metrics in production; identify and drive continuous improvements to model accuracy, latency, and efficiency.
Data Pipeline Engineering: Build and maintain scalable data pipelines for feature engineering and model training across large-scale structured and unstructured datasets.
Experimentation: Design and analyse A/B tests and offline experiments to evaluate model quality and drive continuous improvement.
Cross-Functional Collaboration: Work closely with Data Scientists, Engineers, and Product teams across a multi-functional, global team structure to align ML delivery with business objectives.
Research & Innovation: Evaluate emerging ML and MLOps research for potential adoption within existing systems, including Gen AI investigations and exploration relevant to the personalisation domain.
What skills do you need to play your role well?
Demonstrated hands-on experience across the full ML lifecycle: pipeline development, model training, testing, deployment, serving, monitoring, and maintenance.
Proficiency in Python and familiarity with ML libraries (e.g. TensorFlow, PyTorch, Keras).
Practical experience with production ML pipeline frameworks — TFX, Kubeflow Pipelines SDK, or Airflow-orchestrated training pipelines. Note: experience with TensorFlow, Keras, Spark, or NLTK alone does not meet this requirement.
Hands-on experience with model serving technologies (e.g. TensorFlow Serving, Triton Inference Server, TorchServe) in a production environment.
Experience deploying ML models at meaningful production scale — high-volume, real-world traffic, with measurable business impact.
Familiarity with cloud-based ML infrastructure, particularly Google Cloud Platform (Vertex AI).
Solid understanding of recommendation system design and personalisation algorithms.
Experience with high-volume data processing and streaming architectures.
Good communication and analytical problem-solving skills.
Desirable: Experience with Generative AI in a production ML context.
How do you land the role?
We like to keep our recruitment process simple, transparent, and respectful:
First touch: An open chat with one of our recruiters about your experience, goals, and motivation.
First interview: A conversation with your future manager or teammates about the role and team.
Technical interview: A chance to demonstrate your skills on real-world problems, no trick questions.
Culture check: For most roles, a casual lunch or coffee with the team. For managers, a discussion with the manager’s manager.
What can you expect in return?
Global Impact: Work in an international environment on cutting-edge technology that scales globally.
People-First Culture: We care about our people just as much as we care about the stability of our platform.
Performance Bonuses: Earn an annual bonus based on your performance.
Hybrid Work: Enjoy the best of both worlds with a mix of office and home working.
Work-Life Balance: Flexible working hours to help you balance work and life.
25 days of holidays.
5 days of on-demand leave (sick days).
2 days of paid community volunteering leave.
1 day of paid leave for moving house.
Wellbeing Allowance: 18,000 CZK per year to invest in your personal wellbeing.
Fitness Perks: Get a fully covered Multisport card or a 950 CZK monthly contribution to a Benefit Card.
Meal Allowance: 225 CZK per day to keep you fueled.
Premium Life Insurance: Enjoy peace of mind with our premium life insurance scheme.
Fun Perks: Free tickets to Universal Theme Parks.