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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 /
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.