Are you ready to turn machine learning ideas into reliable solutions that improve how products are made? As a Machine Learning Engineer, you will work within a collaborative technical team to build, deploy, monitor, and maintain machine learning solutions that create measurable business value. You will partner with data scientists, analytics leaders, IT, and manufacturing teams to move models from experimentation into scalable and reliable production environments. This role is based in Monterrey and requires regular onsite presence, with hybrid flexibility. Participate in the development and maintenance of end-to-end machine learning pipelines, including data ingestion, preprocessing, training, validation, deployment, monitoring, and retraining. Collaborate with data scientists and engineers to translate prototypes and experimental models into production-ready solutions. Support model serving through APIs, batch jobs, or real-time systems, and apply MLOps practices for versioning, orchestration, monitoring, and CI/CD. Troubleshoot data, model, deployment, and integration issues while maintaining clear technical documentation and participating in code reviews. Bachelor's degree in Computer Science, Engineering, Data Science, Software Engineering, Data Engineering, or a related technical field. 0-2 years of experience in machine learning,



data science, data engineering, software engineering, or relevant hands‐on academic, internship, personal, or professional projects. Strong Python foundation and hands‐on experience with at least one machine learning library or framework such as scikit‐learn, TensorFlow, or PyTorch. Understanding of the machine learning lifecycle and the ability to clearly explain a project, your personal contribution, the tools used, and the outcome. Basic familiarity with data pipelines, databases, APIs, software development practices, or workflow automation. Advanced technical and business English, both written and verbal. Ability to work onsite in Monterrey at least two days per week and support plant‐based projects as needed. Exposure to Databricks, MLflow, Kubeflow, Docker, Kubernetes, CI/CD, or other MLOps/DevOps tools. Experience with manufacturing, industrial, process, plant, or production data. A GitHub portfolio or other examples that demonstrate hands‐on technical work. Opportunity to work on high‐impact machine learning initiatives that support manufacturing and business transformation. Collaborative general environment with exposure to Data Science, IT, analytics, and manufacturing teams.

📌 Machine Learning Performance Engineers (Monterrey)
🏢 Corning
📍 Monterrey