
Automotive ⢠Artificial Intelligence ⢠SaaS
COMPREDICT is a company specializing in virtual sensor technology to transform vehicle data into actionable insights. Through purely software-based solutions, COMPREDICT enhances existing hardware with virtual sensors, enabling new measurement capabilities, vehicle health, and usage monitoring. Their platform is designed for vehicle OEMs, tier 1 suppliers, and fleet solution providers, offering predictive maintenance technology and optimizing component lifecycle. The company focuses on reducing the need for physical sensors, saving costs and improving the development process using AI and machine learning. With strategic partnerships, such as those with Renault and UTAC, COMPREDICT is at the forefront of advancing mobility through innovative data-driven solutions.
11 - 50 employees
đ¤ Artificial Intelligence
âď¸ SaaS
đ° $15M Series B on 2024-06
October 29

Automotive ⢠Artificial Intelligence ⢠SaaS
COMPREDICT is a company specializing in virtual sensor technology to transform vehicle data into actionable insights. Through purely software-based solutions, COMPREDICT enhances existing hardware with virtual sensors, enabling new measurement capabilities, vehicle health, and usage monitoring. Their platform is designed for vehicle OEMs, tier 1 suppliers, and fleet solution providers, offering predictive maintenance technology and optimizing component lifecycle. The company focuses on reducing the need for physical sensors, saving costs and improving the development process using AI and machine learning. With strategic partnerships, such as those with Renault and UTAC, COMPREDICT is at the forefront of advancing mobility through innovative data-driven solutions.
11 - 50 employees
đ¤ Artificial Intelligence
âď¸ SaaS
đ° $15M Series B on 2024-06
⢠Design and maintain scalable pipelines for deploying machine learning models whether in-cloud or in-vehicle. ⢠Ensure models are securely integrated into production environments with minimal latency. ⢠Implement monitoring systems to track model performance and flag issues. ⢠Develop methods to evaluate and compare the performance of different models. ⢠Automate processes for validating model accuracy and consistency in production. ⢠Work closely with data scientists, developers, and stakeholders to understand their needs and provide tailored solutions. ⢠Create comprehensive documentation for processes, pipelines, and workflows. ⢠Provide training and guidance to team members on MLOps best practices.
⢠Proficient in modern DevOps practices and microservice architecture. ⢠Expertise in Kubernetes and containerization technologies. ⢠Hands-on experience with platforms such as KubeFlow, Kserve, or equivalent. ⢠Experience in ML Experimentation and registry platforms such as W&B or MLFLow. ⢠Understanding of time series modeling and its data requirements. ⢠Familiar with ML/NN frameworks. ⢠Familiar with AWS or other cloud service providers is a plus. ⢠Strong ability to collaborate with cross-functional teams, including data scientists, engineers, and clients. ⢠Clear and concise in verbal and written communication, with excellent documentation skills. ⢠Fluent in both written and spoken English. German is a plus.
⢠Flexible working arrangements ⢠Professional development opportunities
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