Machine Learning Engineer

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.
Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.
MACHINE LEARNING ENGINEER
As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.
You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.
We frequently attack 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.


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 and AWS Relational database management systems Distributed processing such as Apache Spark


Responsibilites
Analyzing and planning problem, solution and delivery
Preprocessing, feature engineering and dataset creation Validation of results Preprocessing, feature engineering and dataset creation Data pipelining and infrastructure development.


Background & Skills
MSc or PhD 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.


Helpful knowledge
Deep learning frameworks and theory Data pipelining and infrastructure Some basic devops stuff, creating and maintaining instances.