Machine Learning Engineer på Modulai AB i Stockholm
Modulai AB · Stockholm
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 tea...
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
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!
About Vionlabs:
Vionlabs is an AI company that helps media and entertainment businesses better understand and recommend video content. Our technology uses machine learning and computer vision to analyze content and improve user experiences for streaming platforms and media companies worldwide.
About the Role:
We are looking for a Machine Learning Engineer to join our growing team in Stockholm.
As a Machine Learning Engineer, you will be responsible for developing, improving, and deploying machine learning models that power our AI-driven products. You will work closely with software engineers, data scientists, and product teams to build scalable solutions that deliver value to customers in the media and entertainment industry.
Responsibilities:
Design, develop, and maintain machine learning models and pipelines
Analyze large datasets and extract actionable insights
Build scalable data processing workflows
Deploy and monitor machine learning models in production environments
Collaborate with engineering and product teams to improve AI-powered services
Continuously evaluate and improve model performance
Qualifications:
Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Mathematics, or a related field
Strong programming skills in Python
Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar
Experience working with large datasets and data processing tools
Knowledge of software engineering best practices
Experience with cloud platforms such as AWS, Azure, or Google Cloud is a plus
Strong analytical and problem-solving skills
Excellent communication skills in English
What We Offer:
Opportunity to work with cutting-edge AI technology
International and collaborative work environment
Competitive compensation and benefits
Flexible working arrangements
Professional development opportunities
Location: Stockholm, Sweden
Employment Type: Full-time, permanent position.
What we're building
OrbDB is building data infrastructure for AI reliability. For every prediction a model makes, the platform determines whether the model is sufficiently certain for the result to be acted on automatically or whether the case should be routed to a human reviewer.
Today’s AI production systems are unable to distinguish which of their predictions are trustworthy. We are building the layer that allows organizations to automate the cases where automation is statistically justified, and to escalate the rest with confidence.
OrbDB is founded and led by researchers with deep expertise in the underlying methods.
The role
You will work on the models that sit at the center of our platform. Our work is built around Graph Neural Networks, and the questions you will engage with are the ones that sit beneath the surface of any serious deep learning system: questions about architecture, training behaviour, optimization, and the relationship between what a model is doing and what we expect it to do.
This is a role for someone who knows the fundamentals of deep learning well enough to reason about them from first principles, not from tutorials. You will work closely with our research-led founding team, and the questions you take on will move between the practical and the foundational, often within the same week. Unlike other AI startups, OrbDB builds on a mathematical foundation. So do the teams behind it. OrbDB Labs is a place where solid ideas and good taste matter more than loud voices.
Specifically, you will:
Train, evaluate, and improve the models that power the platform.
Diagnose model behavior at a level deeper than metrics, and propose changes grounded in the underlying mathematics.
Make principled choices about model design as required.
Work alongside the engineering team to deliver research-grade models into a production system that customers can rely on.
What we are looking for
2-4 years of experience working with deep learning models in a serious technical setting, whether in research, industry, or a combination. If you are close to that range and the rest of the role fits, we would still like to hear from you.
A real command of the fundamentals of deep learning. You should be comfortable reading a paper, implementing it, and reasoning about why a model is or is not behaving as expected.
Strong engineering skills. You write code that others can build on, and you understand that a model is only useful once it runs reliably.
Fluency with the modern deep learning toolchain, particularly PyTorch.
Genuine interest in the statistical foundations of what we are building. Concepts like Conformal Prediction and calibration should be ones you are eager to understand deeply.
Useful, but not required
Experience with Graph Neural Networks specifically, or with the libraries that support them (PyTorch Geometric, DGL, or equivalent).
A graduate degree in a quantitatively rigorous field, or equivalent depth acquired through other means.
Open-source contributions in the ML or deep learning ecosystem, particularly to production-grade libraries.
Experience moving models from research code into production systems.
This is a Stockholm-based hybrid role. Candidates must be living in or willing to relocate to the Stockholm area before starting.
Machine Learning Engineer
Location: Hybrid
Company: Ferritico
Employment type: Full-time
Ferritico is looking for a Machine Learning Engineer to design, develop, deploy, and continuously improve machine learning solutions for advanced materials and steel applications, with a strong focus on production-ready models, data workflows, cloud services, and product integration.
This is a hands-on technical role for someone who enjoys working at the intersection of machine learning, software engineering, data, and industrial product development.
About the role
You will contribute to the development of Ferritico's machine learning models and software platform. The role involves turning industrial and materials data into robust model logic, reliable validation workflows, scalable cloud services, and user-facing product features.
You will work closely with materials engineers, and customers to ensure that machine learning solutions are accurate, maintainable, well-documented, and aligned with real industrial needs.
Key responsibilities
Manage and organize the aggregation, cleaning, and preparation of materials, process, and property data in collaboration with materials engineers.
Design and develop machine learning models and appropriate model structures.
Define model assumptions, evaluation metrics, validation datasets, limitations, and acceptance criteria.
Validate, benchmark, and continuously improve existing and future machine learning models.
Develop and maintain cloud-based machine learning services, training workflows, and inference endpoints.
Monitor production models and troubleshoot performance, reliability, and data-quality issues.
Integrate new machine learning modules into Ferritico's web application.
Support customers in running simulations, understanding model outputs, and identifying suitable machine learning solutions for their processes.
Contribute to testing, technical documentation, code reviews, and engineering decision-making.
What we are looking for
We are looking for someone with a strong background in machine learning, data science, computer science, mathematics, engineering, artificial intelligence, or a related quantitative field.
The ideal candidate has:
An MSc, PhD, or equivalent practical experience in a quantitative field such as Computer Science, Mathematics, Engineering, Artificial Intelligence, or a related discipline.
Strong proficiency in Python and experience building clear, maintainable, and well-tested code.
Practical experience with pandas, scikit-learn, and common workflows for data preparation, model development, evaluation, and deployment.
Solid understanding of statistical modeling, machine learning methods, validation strategies, and performance metrics.
A basic understanding of backend and frontend development and how machine learning components integrate into software products.
Rigorous attention to detail, strong communication skills, and the ability to take ownership of high-quality deliverables in a collaborative team.
Nice to have
Experience with any of the following would be highly valuable:
Google Cloud Platform, cloud hosting, containerized services, or MLOps workflows.
Git-based version control, automated testing, continuous integration, and production monitoring.
Physics-informed machine learning, scientific computing, or models that incorporate domain constraints.
Materials engineering, metallurgy, steel-industry data, or other industrial engineering applications.
Customer-facing technical work, SaaS products, web applications, or translating business and process needs into machine learning solutions.
This role could be a strong fit if you
Have recently completed an MSc or PhD involving machine learning, statistical modeling, artificial intelligence, or scientific computing.
Have practical experience developing, validating, deploying, or maintaining machine learning models.
Enjoy combining data science with software engineering and practical product development.
Are an ambitious and independent learner who takes responsibility for results while collaborating closely with others.
Are excited about helping shape digital tools for the future of steel and advanced materials.
Why join Ferritico?
At Ferritico, you will join a Swedish software startup working at the frontier of materials science, AI, and industrial digitalization. Built on more than 10 years of research at KTH, our SaaS platform helps steel companies accelerate the development, manufacturing, and implementation of advanced alloys.
You will have significant responsibility and autonomy, work with a small multidisciplinary team, and influence both the machine learning foundation and product direction of a platform used in industrial production. We value teamwork, curiosity, technical excellence, and clear communication.
Not sure you meet every requirement?
We encourage you to apply even if your experience does not match every qualification listed above. We value diverse backgrounds, different perspectives, and people who are motivated to learn and contribute.
How to apply
Please send your CV and a short note describing your motivation for the role, along with your relevant experience in machine learning, data science, software engineering, or industrial applications, to: contact@ferritico.com (Please include “Machine Learning Engineer” in the email subject line)
Application deadline: 31 July 2026
