Senior Machine Learning Engineer - Sweden 🇸🇪

<p><strong>SENIOR MACHINE LEARNING ENGINEER - Sweden</strong></p><p><strong><br></strong></p><p><strong>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. As a Senior MLE you will be responsible for team success.</strong><br></p><p><strong>As a senior ML Engineer, 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 be even more successful. <br></strong><strong><br></strong></p><p>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.</p><p>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.<br><br><strong>NOTE</strong>:<br><strong>To Apply we require a work VISA for Sweden or Switzerland</strong>. Currently, we do not offer sponsorships.</p><p> </p><p><strong>Responsibilities</strong><strong><br></strong></p><ul><li>Helping the team to succeed</li><li>Identifying and executing opportunities (new business, new products)</li><li>Challenge and inspire the team in state-of-the-art applied ML</li><li>Analyzing and planning problems, solutions, and delivery</li><li>Preprocessing, feature engineering, and dataset creation</li><li>ML model development</li><li>Validation of results</li><li>Data pipelining and infrastructure development<strong></strong></li></ul><p><strong></strong><strong><br></strong></p><p><strong>Our Stack</strong><strong><br></strong></p><ul><li>Python – standard open-source libraries</li><li>Scikit-learn and various specialized Python and R ML libraries</li><li>Deep learning frameworks such as PyTorch and Tensorflow/Keras</li><li>Cloud platforms such as GCP, AWS, Azure</li><li>Relational database management systems</li><li>Distributed processing such as Apache Spark</li></ul><p><strong></strong><strong><br></strong></p><p><strong>Background &amp; Skills</strong><strong><br></strong></p><ul><li>MSc or Ph.D. in a quantitative field</li><li>Experience in leading end-to-end ML projects</li><li>Excellent communicator (IRL, blog, events, etc)</li><li>Excellent understanding of a broad set of ML algorithms and frameworks</li><li>A passion for lean, clean, and maintainable code</li><li>The desire to grow and to share insights with others</li></ul><p><strong><br></strong></p><p><strong>Helpful knowledge &amp; Experience</strong><strong><br></strong></p><ul><li>Leadership in fast-growing organisations</li><li>Product experience from idea to MVP and monetization.</li><li>Deep learning frameworks and theory</li><li>Data pipelining and infrastructure</li><li>DevOps experience, CI/CD, Kubernetes</li></ul><p><strong></strong><strong><br></strong></p><p><strong>About Team Modulai</strong><strong><br></strong></p><p>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. </p><p>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!</p><p> </p> •

Last updated on Mar 10, 2021