... internship, personal, or professional projects. - Strong Python foundation and hands-on experience ... What would be a plus? - Exposure to Databricks, MLflow, Kubeflow, Docker, Kubernetes, CI/CD, or ...
... internship, personal, or professional projects. - Strong Python foundation and hands-on experience ... What would be a plus? - Exposure to Databricks, MLflow, Kubeflow, Docker, Kubernetes, CI/CD, or ...
Databricks Internships information
What is the difference between Databricks Internships vs Data Engineer Internships?
| Aspect | Databricks Internships | Data Engineer Internships |
|---|---|---|
| Required Skills | Knowledge of Spark, cloud platforms, programming (Python, Scala) | Data pipeline development, SQL, cloud services, programming (Python, Java) |
| Work Environment | Collaborative, tech-focused, cloud-based platforms | Data processing, database management, cloud infrastructure |
| Industry Usage | Tech companies using Databricks platform | Organizations managing large-scale data systems |
Databricks Internships typically focus on working with the Databricks platform, Spark, and cloud-based data solutions. Data Engineer Internships involve building and maintaining data pipelines, working with databases, and cloud infrastructure. Both roles require programming skills and familiarity with cloud services, but Databricks Internships are more platform-specific, while Data Engineer Internships cover broader data engineering tasks.
What are the key skills and qualifications needed to thrive as a Databricks Intern, and why are they important?
To thrive as a Databricks Intern, you should have a solid background in computer science, programming (especially Python, Scala, or SQL), and data analytics, often supported by relevant coursework or project experience. Familiarity with big data platforms like Apache Spark, cloud services (AWS, Azure), and version control systems such as Git is highly beneficial. Strong problem-solving skills, eagerness to learn, and effective communication help interns collaborate and grow within diverse technical teams. These skills and qualities are crucial for contributing to real-world data projects and maximizing the learning experience during the internship.
What types of projects and responsibilities can Databricks interns expect to work on during their internship?
Databricks interns typically work on impactful, real-world projects alongside full-time engineers or data scientists. You may contribute to product features, optimize data pipelines, or help improve machine learning models, depending on your team placement. Interns are encouraged to collaborate cross-functionally, participate in code reviews, and attend team meetings, gaining exposure to agile development practices and cloud technologies. This hands-on experience fosters strong technical growth and provides valuable insight into fast-paced, collaborative tech environments.
What are Databricks internships?
Databricks internships are structured programs that offer students and recent graduates the opportunity to gain hands-on experience working with data engineering, machine learning, and cloud technologies at Databricks. Interns typically work on real-world projects alongside experienced engineers, data scientists, and business professionals. These internships provide mentorship, training, and networking opportunities, and are a valuable way to learn about the tech industry and Databricks’ culture. They are available in a variety of roles, including software engineering, product management, and data science.
What are popular job titles related to Databricks Internships jobs in Michigan? For Databricks Internships jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Databricks Internships jobs in Michigan look for? The top searched job categories for Databricks Internships jobs in Michigan are:
What cities in Michigan are hiring for Databricks Internships jobs? Cities in Michigan with the most Databricks Internships job openings:
Corning rating
8.2
Based on 128 frontline employees who took The Breakroom Quiz
87th of 537 rated manufacturers
Job description
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