[Ejy182] Staff Machine Learning Engineer Solo Quedan 24H

Machine Learning (ML) and Artificial Intelligence (AI) are revolutionizing the way of doing business on a global scale. sennder is a European digital freight forwarder with a data-centric problem-solving approach to build the next generation of supply chain and road logistics services. We are looking for a Staff Machine Learning Engineer to join our central Machine Learning Engineering teams as part of the sennAI department. The department's mission is to achieve "Automated & Data-Driven Road Logistics." We're a large, diverse, and multidisciplinary group of ML & AI engineers, data scientists, backend/frontend engineers, and technical product people who are passionate about the new AI-empowered digitalization wave that is changing our world. Our purpose is to build proprietary technology that can automate sales, brokerage, and other business-related activities. Such automation can enable a flywheel where data acquisition and revenues grow exponentially with one another.The scope of our teams is creating best-in-class predictive analytics services while approaching ML Engineering in a holistic, end-to-end fashion: from best practices in ML modeling to engineering excellence around our MLOps Platform that enhances the developer experience. Every day, we acquire 3M+ new real-time data points about the road logistics industry in Europe. This data is used to build the future of logistics marketplaces where pricing optimization, load-to-carrier recommendations, load search, and network optimization happen in an automated fashion.IN THIS ROLE YOU WILL:Define the new state-of-the-art for machine learning engineering in road logistics services.Apply data science concepts to solve problems such as pricing optimization, load-to-carrier recommendation, load search, and logistics network optimization.Mentor junior to senior engineers, enabling them toward successful and impactful software deliveries.Review technical roadmaps and deliveries across teams.Design and develop health and performance monitoring tools (MLOps) for data pipelines and machine learning services in production.Lead design reviews with peers and stakeholders to decide among available technologies.Be hands-on when needed while reviewing code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).Let's #keepOnTrucking together!
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