[EJY182] Staff Machine Learning Engineer Solo Quedan 24H
Responder al anuncioMachine 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).
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