Advance MLOps practices and build scalable machine learning systems that impact the everyday lives of users on a two-sided mobility platform.
Location: We are flexible! Come and join us either in Hamburg, Berlin, Barcelona or Athens.
Freenow empowers smarter mobility decisions helping people to move freely and cities to thrive.
The Freenow marketplace is a real-time, two-sided platform connecting millions of riders and drivers. Its efficiency and scalability are powered by a sophisticated ecosystem of Machine Learning systems spanning key domains such as Pricing, Allocation, and Marketplace Payment&Risk.
In this role, you’ll help advance our MLOps practices, build the services and the infrastructure that scales ML models from research to real-time production and observe how they impact the daily lives of our users and our business.
Be ready to work in a multinational, diverse, highly motivated and collaborative team of passionate developers who strive for excellence and like to have fun doing it. Are you ready for your next ride?
YOUR DAILY ADVENTURES WILL INCLUDE:
Our Techstack: Python | Airflow | Java | Kubernetes | Kafka | Databricks |AWS & more
TO BE SUCCESSFUL IN THIS ROLE:
Plus more local benefits depending on your work location!
Freenow is an equal opportunity employer and we consider qualified applicants regardless of race, religion, national origin, gender, gender identity, sexual orientation, disability or age.
We want you to grow and evolve, bring your true self to work.
Flexible Work Hours
Flexible working arrangements
Health Insurance
Other Benefit
Employee assistance program
FREENOW is Europe's leading multi-mobility platform that combines taxis, private cars, eBikes, and eScooters into a single app, available in over 100 cities. We cater to urban dwellers and travelers looking for efficient and sustainable transportation options, leveraging advanced technology to make mobility smarter and more accessible.
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Understand the required skills and qualifications, anticipate the questions you may be asked, and study well-prepared answers using our sample responses.
Machine Learning Engineer Q&A's