Machine Learning Engineer
Joinusand builda streamingplatformusedby millions.
At Sky Czech Republic, we’rebuilding 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 ourglobal streaming platform-a kind of technologicalskeleton where each service plugs in its own content and branding. Our platform serves millions of users worldwide. Just to give you an idea-Peacock alonehas40 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’tjust keep the engine running-we push the tech boundaries of what’spossible, 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 maintainproduction-grade ML training pipelines using orchestration frameworks (TFX, Kubeflow Pipelines SDK, Airflow), handling the full lifecycle from feature engineering through to model testing, validation, evaluationand 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 operateML 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; identifyand drive continuous improvements to model accuracy, latency, and efficiency.
Data Pipeline Engineering: Build and maintainscalable 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 MLOpsresearch for potential adoption within existing systems, including Gen AI investigations and exploration relevant to the personalisation domain.
Whatskillsdo youneedto playyourrole well?
Demonstrated hands-on experience across the full ML lifecycle: pipeline development, model training, testing, deployment, serving, monitoring, and maintenance.
Proficiencyin 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 communicationand analytical problem-solving skills.
Desirable: Experience with Generative AI in a production ML context.
Howdo youlandtherole?
Weliketo keepourrecruitmentprocesssimple, transparent, and respectful:
Firsttouch: Anopenchat withoneof ourrecruitersaboutyourexperience, goals, and motivation.
Firstinterview: A conversationwithyourfuturemanager or teammatesabouttherole and team.
Technicalinterview: A chanceto demonstrateyourskillson real-worldproblems, no trickquestions.
Culturecheck: Formost roles, a casuallunchor coffeewiththeteam. Formanagers, a discussionwiththemanager’smanager.
Whatcanyouexpectin return?
GlobalImpact: Workin aninternationalenvironmenton cutting-edgetechnologythatscalesglobally.
People-FirstCulture: Wecareaboutourpeoplejust as muchas wecareaboutthestability of ourplatform.
PerformanceBonuses: Earnanannualbonus basedon yourperformance.
Hybrid Work: Enjoythebestof bothworldswitha mix of officeand homeworking.
Work-LifeBalance: Flexibleworkinghoursto helpyoubalanceworkand life.
25 daysof holidays.
5 daysof on-demandleave(sickdays).
2 daysof paidcommunityvolunteeringleave.
1 dayof paidleaveformovinghouse.
WellbeingAllowance: 18,000 CZK per yearto investin yourpersonalwellbeing.
Fitness Perks: Get a fullycoveredMultisportcardor a 950 CZK monthlycontributionto a Benefit Card.
MealAllowance: 225 CZK per dayto keepyoufueled.
Premium LifeInsurance: Enjoypeaceof mindwithourpremiumlifeinsurancescheme.
FunPerks: Freeticketsto UniversalThemeParks.