Machine Learning Engineer

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  Perk s : 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 . — Published by Sky. Apply on the employer's own posting.