Obispado, NLE, MX, 64060
Open 4 months, first seen by us 126 days ago- 0-2 years of experience in machine learning, data science, data engineering, software engineering, or relevant hands-on academic, internship, personal, or professional projects
Build and maintain end-to-end ML pipelines, from data ingestion to retraining
Turn prototype and experimental models into production-ready solutions
Support model serving through APIs, batch jobs, or real-time systems
Troubleshoot data, model, deployment, and integration issues
Write technical documentation and take part in code reviews
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
Troubleshoot data, model, deployment, and integration issues
maintaining clear technical documentation and participating in code reviews
What do you need to have? - 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.
Open 4 monthsWe first saw this posting 126 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
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Day 126
Up longer than most. Similar jobs usually come down between day 8 and day 76.
Well past Corning’s usual: its jobs come down by about day 42.
From 5,460 similar postings that came down, and 85 of Corning’s. Coming down is not always a hire.
26 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- 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.
What would be a plus? - 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.
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New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Are you ready to turn machine learning ideas into reliable solutions that improve how products are made?
What is your role?
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.
Major responsibilities and tasks of the position:
- 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.
What do you need to have?
- 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.
What would be a plus?
- 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.
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
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Obispado, NLE, MX, 64060
What is your role? 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. Major responsibilities and tasks of the position:
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