Machine Learning Engineer
vor 1 Woche
MACHINE LEARNING ENGINEER - Switzerland
As a member of the machine learning team, you will be working with a broad range of problems with one common denominator – ML will be the key ingredient.
You will have to analyze the problem at hand, come up with a solution strategy, and execute it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data, and then re-formulating it as an ML problem. It requires openness, creativity, and an eagerness to learn new methodologies and explore new terrains.
We approach these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.
NOTE:
We are looking for ML Engineers with +2 years of experience with ML in production.
To Apply we require a work VISA for Sweden or Switzerland. Currently, we do not offer sponsorships.
Our Stack
- Python / R – standard open-source libraries
- Scikit-learn and various specialized Python and R ML libraries
- Deep learning frameworks such as PyTorch and Tensorflow/Keras
- Cloud platforms such as GCP, AWS, and Azure
- Relational database management systems
- Distributed processing such as Apache Spark
Responsibilities
- Analyzing and planning problems, solutions, and delivery
- Preprocessing, feature engineering, and dataset creation
- ML model development
- Validation of results
- Data pipelining and infrastructure development
Background & Skills
- MSc or Ph.D. in a quantitative field
- Excellent understanding of a broad set of ML algorithms and frameworks
- A passion for lean, clean, and maintainable code
- The desire to grow and to share insights with others
- Experience from ML in production
Helpful knowledge
- Deep learning frameworks and theory
- Data pipelining and infrastructure
- DevOps experience, CI/CD, Kubernetes
About Team Modulai
At Modulai, we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis, for clients, as part of the core team in startups where we have long-term engagements, and we also build our own ML products.
Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but never do it because we always work in teams. If there is data, we will do ML on it
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