About XITE
It’s our mission to share our passion for music videos with the world and invite people to experience music beyond listening: to see your music.
XITE is dedicated to building the best music video experience in the world. Based in Amsterdam, XITE now reaches 100 million households across multiple territories through linear networks, interactive TV apps and on-demand streaming services. The company revolutionised the way audiences experience music videos by allowing viewers to search, like and skip through its entire catalogue, enjoy channels curated by its team of music experts and create their own channel based on a genre, decade, style or mood using its unique Mixer feature. XITE has a full music video catalogue through agreements with all major and top independent music labels and distributors, and works to expand this offering every day.
About the role
We are looking for a passionate Mid+/Senior Python engineer in the Data/ML domain to join us. Together with the rest of the team you will be responsible for our machine learning services in production: software and service design/development, monitoring, practical MLOps, performance and architecture. Our technical stack includes Python, Scala, Kafka, ClickHouse, Docker, Bazel, Kubernetes, GCP, Superset and many other, mostly open source, technologies. To accomplish your goals you will take a core role for a team of data and machine learning engineers, working closely with the Data Science, Data Analytics, and Backend Engineering teams, as well as other technical teams and non-technical stakeholders within XITE.
Responsibilities
Design, develop and scale new/existing data and machine learning pipelines (ETLs, feature stores, productize model training and serving), data products and services.
Resolve problems, with end-to-end ownership of machine learning and data preparation/transformation pipelines.
Assist colleagues across technical challenges.
Design, test, install and maintain highly scalable and data-intensive systems.
Review, maintain, refactor and extend distributed systems in production. Support other teams for usage and integration with those systems.
Maintain the technical excellence of the data, ML and software engineering practices.
Work on shared libraries, infrastructure and building blocks within monorepo space.
Work with the Product Manager and other stakeholders, taking part in forming, prioritising and executing data intensive products backlog.
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