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Mlops Machine Learning Engineer Jobs (NOW HIRING)

Corning

As a Machine Learning Engineer, you will work within a collaborative technical team to build ... systems, and apply MLOps practices for versioning, orchestration, monitoring, and CI/CD ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Manage MLOps infrastructure to monitor and optimize models. Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

Pattern AI

Machine Learning Engineer

San Mateo, CA

Manage MLOps infrastructure to monitor and optimize models. Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

Wipro Limited

mlops Engineer

Richfield, PA · On-site

$60K - $135K/yr

  • Medical

  • Dental

  • PTO

... machine learning systems and pipelines. You will collaborate closely with data scientists, software ... MLOps, Machine Learning Engineering, or a related field Mandatory Skills: GCP AI ML MLOps.

Leash Bio

Machine Learning Engineer

Salt Lake City, UT · On-site

They are seeking a highly skilled Machine Learning Engineer to manage large datasets, optimize ... Preferred : • Familiarity with common MLops tooling (e.g., Dagster, Prefect, Airflow, Docker ...

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team ... Apply MLOps best practices for training, evaluation, deployment, and monitoring of production grade ...

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Mlops Machine Learning Engineer information

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$31.5K

$128.8K

$193.5K

How much do mlops machine learning engineer jobs pay per year?

As of Aug 13, 2026, the average yearly pay for mlops machine learning engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What does an MLOps machine learning engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
More about Mlops Machine Learning Engineer jobs
What cities are hiring for Mlops Machine Learning Engineer jobs? Cities with the most Mlops Machine Learning Engineer job openings:
What states have the most Mlops Machine Learning Engineer jobs? States with the most job openings for Mlops Machine Learning Engineer jobs include:
What job categories do people searching Mlops Machine Learning Engineer jobs look for? The top searched job categories for Mlops Machine Learning Engineer jobs are:
Mlops Machine Learning Engineer jobs near you
Infographic showing various Mlops Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

Corning

On-site

Full-time

Re-posted 3 days ago

Corning rating

8.2

Company rating: 8.2 out of 10

Based on 128 frontline employees who took The Breakroom Quiz

88th of 538 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 

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