Senior Machine Learning Engineer - Sweden 🇸🇪

Senior Machine Learning Engineer - Sweden 🇸🇪 As a Modulai Senior ML Engineer you will be responsible for leading your team or advising many teams in solving some of the worlds most challenging problems in applied ML. Apply for this job! We usually respond within two weeks SENIOR MACHINE LEARNING ENGINEER (SMLE) - Sweden As a member of Modulai you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. As a SMLE you will be responsible for team success and advisory to both teams and clients. In this role, you will be responsible for engaging clients and teams from the initial idea phase through to execution. We are looking for someone with a proven track record of successfully developing end-to-end ML products and leading teams in both commercial and delivery settings. It's important that you have a passion for ML and strong opinions on how to succeed in applied ML. At our company, being a lead is not just a formal title; it's a responsibility to help others achieve even greater success. You will have to analyze the problem at hand, devise a solution strategy, and execute it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data, and then re-formulating it as an ML problem. It requires openness, creativity, and an eagerness to learn new methodologies and explore new terrains. We approach these problems as a team where great leaders are essential, meaning that you will have to be able to clearly explain your reasoning and code to engage the team as well as clients, investors, and others. NOTE : To Apply we require a work VISA for Sweden . Currently, we do not offer sponsorships. Responsibilities Helping the team to succeed as a technical mentor Challenge and inspire the team in state-of-the-art applied ML Analyzing and planning problems, solutions, and delivery Preprocessing, feature engineering, and dataset creation ML model development Validation of results Data pipelining and infrastructure development Our Stack Python – standard open-source libraries Scikit-learn and various specialized Python and R ML libraries Cloud platforms such as GCP, AWS, Azure Relational database management systems Distributed processing such as Apache Spark Background & Skills MSc or Ph.D. in a quantitative field 5 years of experience in leading end-to-end ML projects Experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). Experience with software development in one or more programming languages, and with data structures/algorithms. Excellent communicator (IRL, blog, events, etc) 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 & Experience Leadership in fast-growing organisations Product experience from idea to MVP and monetization. Deep learning frameworks and theory Data pipelining and infrastructure DevOps experience, CI/CD, Kubernetes 2 years of experience with state-of-the-art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts About Team Modulai At Modulai, we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis, for clients, as part of the core team in startups where we have long-term engagements, and we also build our own ML products. 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 never do it because we always work in teams. If there is data, we will do ML on it! Teams ML engineering Role Senior Machine Learning Engineer Locations Stockholm Remote status Hybrid Stockholm Bringing fun to work We enjoy traveling together, visiting innovative companies and organizations, new cities, and memorable places. We spend an incredible amount of time of our lives at work; therefore, we believe that we become more successful as a team and business by incorporating fun into work. Daily life at work involves breaks for video games, dog cuddles, and just hanging out. We are a group of friends and make sure to experience life together both inside and outside of company life. Yearly skiing trip, visiting AI conferences and company kickoffs have become appreciated traditions by now.