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

Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI

Cloud platforms such as AWS, GCP, and Azure

CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,

Relational database management systems

MLOps and LLMOps tools for model deployment and monitoring.

Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)

Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform