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

Responsibilities

Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client

Preprocessing, feature engineering, and dataset creation

ML and LLM model development, fine-tuning, and evaluation

Validation of results and model interpretability

Building and optimizing data pipelines and ML/LLM infrastructure

Developing APIs and integrating ML models into production systems

Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

MSc or Ph.D. in a quantitative field

Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs

Strong software development skills in Python and experience with software engineering best practices

Experience deploying ML and LLM models into production environments

A passion for lean, clean, and maintainable code

The desire to grow and to share insights with others

Helpful Knowledge

Deep learning frameworks and transformer-based architectures

LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)

Data pipelining and ML/LLM infrastructure best practices

DevOps experience, CI/CD, Kubernetes, and serverless architectures

Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

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 actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/