Stellenbezeichnung: MB.OS Mercedes-Benz Operating System – Machine Learning Engineer (f/m/x)
Arbeitsort / Location: Sindelfingen, Baden-Württemberg
Job Beschreibung: The Department Data & AI (RD/CUD) plays a central role in the digital transformation of Mercedes-Benz AG. A wide variety of rich data sources including data from our test and endurance car fleets, our customer car fleet, from test benches, workshops, production lines and external suppliers defines substantial potential for our business. Such potential needs to be concretely identified, consolidated within the company and finally harvested to offer the best service to our customer and to keep the pace in the digital business as a modern, data-driven company.
The Team Data Insights consists of roundabout 12 highly-motivated individuals, looking forward to welcome you in their ranks. We deal with multiple data streams going in and out of the car, ensure the stability and quality of those streams, analyze them and extract the information which offers our customers the best possible product. We are working with state-of-the-art data and information technologies, together with international partners in Europe, US, India and China. With us you will take responsibility of projects and work on topics with a significant impact on the whole organization.
Your position will come with the following tasks and responsibilities:
- To identify data potential in the company and to contribute to the development of data products which generate measurable business value
- To connect existing data sources, to make them generally available and efficiently useable to relevant stakeholders
- To explore the latest methods and technology from the area of artificial intelligence, esp. machine learning and to implement them in our software-driven data products
- To not only develop end-to-end data products but also deploy, run and maintain them in a professional IT infrastructure, mainly in the cloud
Your profile should cover the following areas of expertise:
- Is the ability to solve complex questions based on data with mathematical and esp. statistical methods – programming in Python (or R, C, etc.) is key and the focus is on modelling techniques as mathematical problem with the help of known data science techniques (supervised, unsupervised, reinforcement learning)
- Is the ability to quickly comprehend complex data structures, tables, formats and correlations, to eventually join and further process them – a base requirement to do so is the big data tool stack such as Spark (Databricks)
- The data engineer is building data pipelines in such a way that AI-based models can be trained on the data – crucial for this is a thorough understanding of the underlying business processes and to incorporate them in the data preparation
- The ability to write software that is fully operationalizable – in a ML/DL context this is currently mainly Python code and entails the initial build-up and structuring of code products via version control (e.g. git and GitHub), as well as testing and deployment pipelines via CI/CD frameworks (e.g. Azure DevOps) – the last main ingredient is to ensure proper code documentation and robustness
- The ability to develop cloud (or on-prem) data product architectures – different IT components must work together seamlessly for the underlying software to work properly
- The solution architect is responsible to know all relevant services and their interactions and ensures their most efficient use in order to create cost-effective and robust application landscapes, together with a lean maintenance and operations model
What you bring to the table:
- Completed academic degree in Computer Science, Mathematics, Physics or a similar technical discipline. A PhD is welcome but not required
- Excellent programming skills in Python – expertise in at least one other language such as C, C++, Java or R is welcome but not required
- Solid experience with software development best practices, specifically with version control via e.g. git and GitHub, Python programming style and documentation standards such as PEP8 and numpydoc, as well as methods for software quality control such as pair coding or functional testing
- Documented knowledge of common machine learning frameworks such as scikit-learn, XGBoost, Tensorflow, PyTorch, OpenCV and MLFlow
- Documented knowledge with data bases and distributed file systems such as SQL (and its variants), Hadoop HDFS, Pandas, Spark, Dask, Data -and Delta-Lakes
- Software operationalization on platforms such as Dataiku DSS or Container-based microservice architecture with the help of e.g. Docker and Kubernetes
- Documented experience with Microsoft Azure Cloud Services, specifically ADF, DSVMs, Databricks, AKS, AML and ADLS
It is a permanent position.
It doesn’t work completely without formalities. When sending your online application, please attach your CV, an individual letter and any references you may have (max. 5 MB) and mark your application documents as „relevant for this application“ in the online form.
We particularly welcome online applications from candidates with disabilities or similar impairments in direct response to this job advertisement. If you have any questions, you can contact the local disability officer once you have submitted your application form, who will gladly assist you in the onward application process: email@example.com
Please understand that we no longer accept paper applications and that there is no right to get your documents returned.
If you have any questions regarding the application process, please contact HR Services by e-mail at or the on our career page via the speech bubble symbol at the bottom right.
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