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Machine Learning Ops Engineer Jobs (NOW HIRING)

University of Florida

OPS Engineer

Gainesville, FL · On-site

$35.92 - $40.71/hr

OPS Engineer Job no: 540809 Work type: Temp Full-Time Location: Main Campus (Gainesville, FL ... Strong understanding of the breadth of machine learning to include supervised, unsupervised ...

Paradigm

WI · On-site

$120 - $180/hr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... Deploy and support machine learning models and AI solutions in production, maintaining best ...

iHerb

Principal Machine Learning Engineer

California, MO · On-site

$205 - $230/hr

... Ops engineer, or related position). Education Requirements Bachelor's Degree in Computer Science, Electrical Engineering, or related field required; Master's Degree preferred. Judgment / Reasoning ...

Myparadigm

WI · On-site

$140 - $190/hr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... Deploy and support machine learning models and AI solutions in production, maintaining best ...

Paradigm

Senior ML Ops Engineer

Irving, TX · On-site

$140 - $200/hr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... Deploy and support machine learning models and AI solutions in production, maintaining best ...

Coca-Cola

Manager, Machine Learning Ops

Atlanta, GA · On-site

Experience with code version control platforms like GitHub, GitLab or Azure DevOps. Functional ... in continuous learning to improve our business and ourselves. We focus on four key behaviors ...

Paradigm

Senior ML Ops Engineer

Seattle, WA · On-site

$123K - $170K/yr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... end-to-end machine learning lifecycle, including model training, deployment, monitoring, and ...

Apex Systems

ML Ops Engineer

Dearborn, MI · On-site

$48.50 - $66.50/hr

ML Ops Engineer Duration: Long-Term Contract Location: Hybrid - 4 days/week onsite Description ... Key Responsibilities ML Ops & Machine Learning * Build scalable, secure, and high-performance ML ...

Paradigm

Senior ML Ops Engineer

Denver, CO · On-site

$123K - $170K/yr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... end-to-end machine learning lifecycle, including model training, deployment, monitoring, and ...

Paradigm

Senior ML Ops Engineer

Irving, TX · Remote

$123K - $170K/yr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... end-to-end machine learning lifecycle, including model training, deployment, monitoring, and ...

Paradigm

Senior ML Ops Engineer

Middleton, WI · On-site

$123K - $170K/yr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... end-to-end machine learning lifecycle, including model training, deployment, monitoring, and ...

WTS Paradigm LLC

Senior ML Ops Engineer

Irving, TX · On-site

$123K - $170K/yr

We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are ... end-to-end machine learning lifecycle, including model training, deployment, monitoring, and ...

Diverse Lynx

ML Ops Engineer

Cincinnati, OH · On-site

They are seeking an ML Ops Engineer to develop and deploy solutions using AWS technologies ... Machine Learning certification Company : Diverselynx IT Consulting Services Founded in 2002, the ...

iHerb.com

Principal Machine Learning Engineer

$138K - $185K/yr

... Ops engineer, or related position). Education Requirements: Bachelor's Degree in Computer Science, Electrical Engineering, or related field required, Masters Degree preferred. Judgment/Reasoning ...

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All JobsMachine Learning Ops Engineer Jobs

Machine Learning OPS Engineer information

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$31.5K

$128.8K

$193.5K

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

As of Aug 22, 2026, the average yearly pay for machine learning ops 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 is a machine learning ops engineer?

A Machine Learning Ops Engineer (MLOps Engineer) focuses on deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and software engineering, ensuring models run efficiently, reliably, and at scale. Their responsibilities include automating workflows, managing infrastructure, and ensuring CI/CD pipelines for ML models. They work with tools like Kubernetes, Docker, and cloud platforms to streamline model deployment. Ultimately, an MLOps Engineer ensures that machine learning models are operationalized and continuously improved in a real-world environment.

What does a machine learning ops engineer do?

A typical day for a Machine Learning Ops Engineer involves collaborating with data scientists to streamline the deployment of models, building and maintaining scalable infrastructure on cloud services, and automating workflows with CI/CD tools. You may troubleshoot issues in production environments, monitor model performance, and implement solutions for model versioning and retraining. Often, you’ll work closely with software engineers, DevOps teams, and data analysts to ensure seamless integration of machine learning solutions into products. This cross-functional role keeps you engaged with cutting-edge technology and provides opportunities to influence both technical and business outcomes.

What skills and qualifications are needed to be a machine learning ops engineer?

To thrive as a Machine Learning Ops Engineer, you need a solid grasp of machine learning concepts, cloud platforms, software engineering, and DevOps practices, typically supported by a degree in computer science or a related field. Experience with tools like Docker, Kubernetes, TensorFlow, CI/CD pipelines, and certifications such as AWS Certified Machine Learning – Specialty are highly valuable. Strong problem-solving skills, communication, and the ability to work collaboratively across data science and engineering teams set top candidates apart. These skills ensure reliable deployment, scalability, and optimization of machine learning models in production environments.

Are machine learning ops engineers in demand?

Machine Learning Ops Engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Kubernetes and TensorFlow. The role is expected to grow as organizations prioritize AI-driven solutions and infrastructure automation.
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Machine Learning OPS Engineer jobs near you
Infographic showing various Machine Learning Ops Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Senior Engineer - Data Science

Continental Resources, Inc.

Oklahoma City, OK • On-site

Full-time

Re-posted 5 days ago

Job description

Job Summary
The Senior Engineer, Data Science is a hands-on technical role who designs, builds, and operationalizes advanced analytics and Artificial Intelligence/Machine Learning solutions that drive measurable value across subsurface, drilling and completions, production operations, HSE, and commercial functions at Continental Resources. This role partners with multidisciplinary stakeholders to translate business problems into data-driven solutions, develop robust models and pipelines, and deploy them to production with strong Machine Learning Ops and governance practices. The ideal candidate combines a Master of Science in Data Science with strong applied analytics capability, solid data engineering skills, and practical oil and gas domain experience comparable to a seasoned upstream engineering background.
Duties and Responsibilities
  • Leads the design, development, and deployment of Artificial Intelligence/Machine Learning solutions for upstream subsurface and well operations, including physics-informed and hybrid modeling approaches for reservoir, drilling, and production optimization.
  • Builds advanced Artificial Intelligence/Machine Learning solutions for commercial analytics use cases such as pricing, supply chain, marketing, and trading to improve profitability and decision speed.
  • Executes complex AI initiatives from ideation and discovery through model development, deployment, and sustainment as part of integrated, enterprise-level teams.
  • Architects and implements reliable data pipelines and features using modern data platforms (e.g., Databricks, cloud services), ensuring data quality, lineage, and performance for analytics workloads.
  • Applies Machine Learning Ops best practices to automate training, testing, deployment, monitoring, and model lifecycle management at scale in production environments.
  • Translates complex business problems into analytical approaches with clear hypotheses, success criteria, and measurable outcomes across upstream and commercial domains.
  • Develops and delivers communications that convey a clear understanding of technical concepts, model results, and business implications to diverse technical and non-technical audiences.
  • Builds strong partnerships and cross-functional relationships with geoscience, engineering, operations, commercial, IT, and leadership stakeholders to drive adoption and sustain business impact.
  • Gains the confidence and trust of others through honesty, integrity, and follow-through while championing responsible and secure use of data and AI.
  • Actively seeks new ways to grow and be challenged by staying current on emerging Artificial Intelligence/Machine Learning, generative AI, optimization, and computational techniques relevant to energy and integrating them where they add value.
  • Other duties as assigned.

Skills and Competencies
  • Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
  • Action oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
  • Drives results - Consistently achieving results, even under tough circumstances.
  • Self-development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
  • Nimble learning - Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder.
  • Situational adaptability - Adapting approach and demeanor in real time to match the shifting demands of different situations.
  • Instills trust - Gaining the confidence and trust of others through honesty, integrity, and authenticity.

Required Qualifications
  • Bachelor of Science in Petroleum, Mechanical, Chemical, or related Engineering discipline from an accredited college or university and Master of Science in Data Science, or a closely related data science or analytics field, from an accredited college or university.
  • Minimum five (5) years of hands-on experience delivering production-grade data science/Machine Learning solutions, including end-to-end lifecycle from discovery to deployment and sustainment.
  • Proficiency in Python and SQL; experience with Machine Learning frameworks and tooling (e.g., scikit-learn, PyTorch/TensorFlow), and data platforms such as Databricks and cloud services.
  • Experience building and maintaining data pipelines and features and applying Machine Learning Ops practices for model deployment and monitoring in enterprise environments.
  • Demonstrated ability to partner with technical and business domains in energy, including upstream subsurface, drilling/completions, production operations, and/or commercial analytics such as pricing, supply chain, marketing, or trading.
  • An acceptable pre-employment background and drug test.

Preferred Qualifications
  • Oil and gas industry experience, particularly in upstream engineering, subsurface, drilling and completions, production operations, or commercial energy analytics.
  • Background in computational sciences, optimization, or high-performance computing for engineering applications.
  • Familiarity with enterprise data governance, security, and responsible AI practices in regulated environments.
  • Five (5) or more years of combined oil and gas engineering/domain experience and applied data science experience.

Physical Requirements and Working Conditions
  • Requires prolonged sitting, some bending and stooping.
  • Occasional lifting up to 25 pounds.
  • Manual dexterity sufficient to operate a computer keyboard and calculator.

Continental Resources, Inc. provides equal employment opportunities and access for all applicants and employees without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, national origin, age, disability, genetic information, veteran status, or any other category protected by law.

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