ML Factory Intelligence Engineer

Company Description EntroMetrix develops a physics-informed intelligence layer that simplifies complex industrial operations and drives continuous optimization across production, energy, and materials. By combining advanced analytics with real-world physics, the company helps industrial organizations achieve higher efficiency, lower costs, and improved sustainability. EntroMetrix’s solutions are designed to unlock a new standard of industrial performance by making data-driven decision-making more accurate and actionable. Team members work at the intersection of machine learning, physics, and operations, contributing directly to measurable impact on large-scale industrial systems.Role Description As an ML Factory Intelligence Engineer at EntroMetrix, you will design, implement, and deploy machine learning models that power the company’s physics-informed intelligence layer. You will work on-site full time in the London Area, United Kingdom, collaborating closely with data scientists, software engineers, and domain experts to transform raw industrial data into robust, scalable factory intelligence solutions. Day-to-day responsibilities include building and training neural network and statistical models, implementing pattern recognition algorithms for production and energy optimization, and integrating these models into production systems. You will analyze model performance, run experiments, and iterate on algorithms to improve accuracy, robustness, and latency. The role also involves contributing to system architecture, ensuring data pipelines are reliable and efficient, and translating operational requirements into technical specifications that enhance real-time decision support for industrial clients.Qualifications Strong foundations in Computer Science and Algorithms, with the ability to design efficient, scalable solutions for real-time industrial applications.Demonstrated expertise in Neural Networks and Pattern Recognition for time-series, sensor, or process data, ideally in a production or operations context.Solid background in Statistics and applied mathematical modeling, including model evaluation, uncertainty quantification, and experiment design.Proficiency in one or more programming languages commonly used in ML (e.g., Python, C++), along with experience using ML frameworks such as PyTorch or TensorFlow.Experience building end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, deployment, and monitoring in production environments.Comfort working with industrial or IoT data (e.g., SCADA, historians, sensor streams) and handling large-scale, noisy, or incomplete datasets.Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related field, or equivalent practical experience.Ability to work on-site in the London Area, collaborate in cross-functional teams, and communicate complex technical concepts clearly to technical and non-technical stakeholders.Experience with physics-informed modeling, control systems, optimization, or industrial operations (manufacturing, energy