Sr. Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Corning. Obispado.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and operate ML pipelines from data intake and preparation through deployment, monitoring, troubleshooting and retraining
- Move data science prototypes into production, shaping technical choices for APIs, batch workloads and real-time systems
- Put MLOps and DevOps controls in place for model tracking, orchestration, CI/CD, containers, security and reliability
- Review code and document technical work while guiding junior colleagues and resolving production problems with partner teams
What you bring
- A bachelor's or master's degree in a technical discipline such as computer science, engineering or data science
- At least three years in machine learning, data or software engineering, with hands-on responsibility for production ML systems
- Strong Python skills and practical familiarity with ML libraries such as scikit-learn, TensorFlow or PyTorch
- Working knowledge of the ML lifecycle and MLOps practices, including CI/CD, monitoring, model tracking and containerization
Who this fits
This senior individual-contributor role suits an engineer ready to influence architecture, coach less-experienced teammates and support production systems. You need advanced technical and business English and must work onsite in Monterrey at least two days each week, with plant project support as needed.
From the employer
Are you ready to lead the technical delivery of production-grade machine learning solutions that can transform manufacturing performance?
What is your role?
As a Senior Machine Learning Engineer, you will design, deploy, maintain, and improve robust machine learning systems that support manufacturing and other business functions. You will help translate data science prototypes into secure, scalable, and production-ready solutions while influencing architecture, MLOps practices, and technical standards. This is an individual contributor role based in Monterrey with regular onsite presence and hybrid flexibility.
Major responsibilities and tasks of the position:-
Design, build, and maintain end-to-end machine learning pipelines covering data ingestion, preprocessing, training, validation, deployment, model serving, monitoring, troubleshooting, and retraining.-
Lead the translation of prototypes into scalable production solutions and contribute to architecture and technology decisions for APIs, batch processing, and real-time systems.- Implement MLOps and DevOps practices for model versioning, orchestration, CI/CD, containerization, security, data privacy, and production reliability.
- Guide junior contributors, lead code reviews and technical documentation, and partner with data scientists, IT, analytics, and manufacturing stakeholders to resolve complex production issues.
What do you need to have?
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Software Engineering, Data Engineering, or a related technical field.- At least 3 years of relevant experience in machine learning engineering, data engineering, software engineering, or a related technical role; 3-5 years is preferred.- Proven hands-on experience deploying and supporting machine learning models or systems in production environments.
- Strong Python proficiency and experience with machine learning frameworks or libraries such as scikit-learn, TensorFlow, or PyTorch.- Hands-on understanding of the end-to-end ML lifecycle and MLOps/DevOps concepts, including CI/CD, model versioning, orchestration, monitoring, and containerization.
- Ability to influence architecture and design decisions, troubleshoot complex production issues, and coach or guide less-experienced team members without direct reports.
- Advanced technical and business English, plus the ability to work onsite in Monterrey at least two days per week and support plant-based projects as needed.
What would be a plus?
- Experience with Databricks, MLflow, Kubeflow, Docker, Kubernetes, cloud or on-premise deployment, and enterprise systems integration.- Experience deploying ML solutions in manufacturing, industrial, quality, defect-reduction, or production-optimization environments.- Experience with APIs, model serving infrastructure, relational or non-relational databases, distributed computing, security, and data privacy.
What do we offer?
- Competitive benefits above the requirements of Mexican law.
- Opportunity to work on high-impact machine learning initiatives that support manufacturing and business transformation.
- Collaborative global environment with exposure to Data Science, IT, analytics, and manufacturing teams.
- Learning and career development in a growing technical organization.Corning is committed to providing equal employment opportunities and considers requests for reasonable accommodations in accordance with applicable laws. Individuals with disabilities or sincerely held religious beliefs may request reasonable accommodation to participate in the application or interview process, perform essential job functions, or access other benefits and privileges of employment. To submit a request for reasonable accommodations related to disability or religion, please contact us at accommodations@corning.com