.sennder is Europe's leading digital freight forwarder. In a traditional industry, we are moving fast to digitize and automate all road logistics processes. We move trucks with courage and the power of data to unlock endless and sustainable capacity at exceptional quality. Get to know us better by reviewing our presentation.
Our sennAI team's 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 until engineering excellence around our MLOps Platform that lifts the developer experience to a different realm.
Every day, we acquire 3M+ new real-time data points (augmenting by the day!) about the road logistics industry in Europe. This data is used to build the future of logistics marketplaces where pricing optimization, load-to-carrier recommendation, load search, and network optimization happen in an automated fashion. Can you even imagine where we can go with your help? Let's #keepOnTrucking... together!
WHAT YOU WILL DO As a Staff Machine Learning Engineer in our central Machine Learning Engineering teams within the sennAI department, you will help us in achieving Automated & Data-Driven Road Logistics. You will closely work with our multidisciplinary group of ML&AI engineers, data scientists, backend/frontend engineers, and technical product people that are passionate about the new AI-empowered digitalization wave that is changing our world.
In this role you will:
Define the new state-of-the-art for machine learning engineering in the road logistics services. Apply data science concepts to solve problems like pricing optimization, load-to-carrier recommendation, load search, and logistics network optimization, among others. Mentor junior to senior engineers, enabling them towards successful & impactful software deliveries. Review technical roadmaps and deliveries across teams. Design and develop health and performance monitoring tools (MLOps) of data pipelines and the machine learning services in production. Lead design reviews with peers and stakeholders to decide amongst 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). El anuncio original lo puedes encontrar en Kit Empleo:
https://www.kitempleo.es/empleo/116674787/iu-479-staff-machine-learning-engineer-nw331-barcelona/?utm_source=html
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