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Privacy Preserving Machine Learning Jobs in Michigan

Corning

$95K - $130K/yr

... data privacy, and production reliability. - Guide junior contributors, lead code reviews and ... in machine learning engineering, data engineering, software engineering, or a related technical ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

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Privacy Preserving Machine Learning information

What is privacy preserving machine learning?

Privacy preserving machine learning refers to techniques and methods that allow data analysis and model training while protecting sensitive information. This field focuses on ensuring that personal or confidential data is not exposed or compromised during the development and deployment of machine learning models. Approaches such as federated learning, differential privacy, and homomorphic encryption are commonly used. These methods enable organizations to leverage data for insights and predictions without violating privacy regulations or risking data breaches. Privacy preserving machine learning is especially important in industries like healthcare, finance, and any sector handling personal data.

What are the key skills and qualifications needed to thrive as a privacy preserving machine learning engineer?

To thrive as a Privacy Preserving Machine Learning Engineer, you need a strong background in machine learning, data privacy techniques (such as differential privacy or federated learning), and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow Privacy, PySyft, and privacy-enhancing technologies, along with certifications in data security or privacy, are often required. Strong problem-solving abilities, meticulous attention to detail, and the ability to communicate complex technical concepts clearly set top professionals apart. These skills ensure the development of robust machine learning models that protect sensitive data while delivering valuable insights, maintaining compliance and trust.

What are some common challenges faced by professionals working in privacy preserving machine learning roles?

Professionals in Privacy Preserving Machine Learning often encounter challenges such as balancing model accuracy with strict privacy requirements, selecting appropriate privacy-preserving techniques (like differential privacy or federated learning), and ensuring compliance with evolving data protection regulations. Collaborative projects may also involve coordinating with legal, data security, and software engineering teams to implement robust solutions. Additionally, staying updated with the latest research and adapting to new threats or vulnerabilities is a continuous part of the role.

What is the difference between Privacy Preserving Machine Learning vs Data Scientist?

AspectPrivacy Preserving Machine LearningData Scientist
Required CredentialsTypically requires knowledge of machine learning, data privacy, and security certificationsRequires degrees in data science, statistics, or related fields; certifications like Certified Data Scientist are common
Work EnvironmentWorks in research, development, and implementation of privacy-focused ML models, often in tech or finance sectorsAnalyzes data, builds models, and provides insights across various industries including marketing, finance, and healthcare
Employer & Industry UsageUsed by organizations prioritizing data privacy, such as healthcare, finance, and tech companiesEmployed across diverse sectors for data analysis, predictive modeling, and decision support

Privacy Preserving Machine Learning focuses on developing models that protect data privacy during training and inference, while Data Scientists analyze and interpret data to generate insights. Both roles require strong analytical skills, but Privacy Preserving Machine Learning emphasizes security and privacy techniques, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Privacy Preserving Machine Learning jobs in Michigan?

For Privacy Preserving Machine Learning jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Privacy Preserving Machine Learning jobs in Michigan look for?

The top searched job categories for Privacy Preserving Machine Learning jobs in Michigan are:

What cities in Michigan are hiring for Privacy Preserving Machine Learning jobs?

Cities in Michigan with the most Privacy Preserving Machine Learning job openings:

Privacy Preserving Machine Learning jobs near you
Infographic showing various Privacy Preserving Machine Learning job openings in Michigan as of August 2026, with employment types broken down into 100% Full Time. Highlights an 74% In-person, and 26% Remote job distribution.

Sr. Machine Learning Engineer

On-site

Corning

8.3

Company rating: 8.3 out of 10

Based on 129 frontline employees who took The Breakroom Quiz

66th of 545 rated manufacturers

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Learn more about Corningexternal

$95K - $130K/yr

Full-time

Re-posted 22 days ago

Job description

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 

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