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Mlops Machine Learning Engineer Jobs (NOW HIRING)

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

We are seeking an MLOps Engineer to build, deploy, and optimize machine learning infrastructure that supports scalable, secure, and production-ready AI solutions in cloud environments. Type

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

We are seeking an MLOps Engineer to build, deploy, and optimize machine learning infrastructure that supports scalable, secure, and production-ready AI solutions in cloud environments. Type

Apple

Machine Learning (MLOps) Engineer

Cupertino, CA · On-site

As an MLOps Engineer, you will be the backbone of our machine learning infrastructure, ensuring that AI/ML systems are reliable, scalable, and continuously improving in production. You will bridge ...

Leidos

Senior Machine Learning Engineer

Vista, CA · On-site

$107K - $195K/yr

We are seeking a Senior Machine Learning Engineer to work on MLOPS that support the testing, and release of object detection algorithms for our portfolio of products that help safeguard the flow of ...

Leidos

Senior Machine Learning Engineer

Vista, CA · On-site

$107K - $195K/yr

We are seeking a Senior Machine Learning Engineer to work on MLOPS that support the testing, and release of object detection algorithms for our portfolio of products that help safeguard the flow of ...

Yum! Brands

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

The ideal candidate combines strong software engineering and MLOps fundamentals with a passion for building reliable, scalable machine learning systems. Required Qualifications * Bachelor's or Master ...

Yum Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

The ideal candidate combines strong software engineering and MLOps fundamentals with a passion for building reliable, scalable machine learning systems. Required Qualifications * Bachelor's or Master ...

Ametek

Machine Learning Engineer

Herndon, VA · On-site

We are seeking an early-career Machine Learning Engineer who is excited to grow rapidly by building ... Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

Machine Learning Engineering Engineer 3 Dearborn, MI W2 Position Description: We are seeking an ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Proficiency in Python, MLOps (W&B, MLFlow) and ML packages (scikit-learn, PyTorch, JAX), along with ...

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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 Jul 7, 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, and why are they important?

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 July 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.
Machine Learning Engineer

Machine Learning Engineer

Keysight Technologies, Inc.

Topeka, KS • On-site

Full-time

Posted 21 hours ago

Keysight Technologies rating

8.1

Company rating: 8.1 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

41st of 141 rated electronics manufacturers

Job description

Overview
Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.
Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.
We are seeking a Machine Learning Engineer to lead the design, development, and deployment of scalable machine learning models that power business decisions across the enterprise. This role combines technical depth in ML/AI with a strong understanding of business domains such as Sales, Service, Finance, Order Fulfillment, and Supply Chain. You will collaborate closely with Data Scientists, Data Engineers, and business partners to build production-ready solutions that drive measurable impact.
Responsibilities
1. Machine Learning Development & Deployment
  • Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross-sell/upsell opportunity identification.
  • Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.
  • Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2. Cross-Functional ML Use Cases
  • Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.
  • Develop domain-specific models and continuously improve them using feedback loops and real-world performance data.

3. Model Governance and MLOps
  • Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.
  • Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture
  • Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.
  • Perform feature selection, transformation, and engineering aligned with each domain's business logic.

5. Communication & Stakeholder Collaboration
  • Present technical insights and model results to business and executive stakeholders in a clear, actionable format.
  • Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Qualifications
Required:
  • 4-6 years of experience in machine learning, data science, or AI engineering, with a strong software engineering foundation.
  • Proficiency in Python, and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar.
  • Experience deploying models into production using ML pipelines and orchestration frameworks.
  • Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

Preferred:
  • Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.
  • Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).
  • Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).
  • Background in statistics, forecasting, optimization, or recommendation systems.

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Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***

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