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
• Minimum 2 years of full-time ML exposure, solving real-world problems

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!