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Machine Learning Operations Jobs (NOW HIRING)

SAP

Machine Learning Operations Engineer (MLOps)

Bellevue, WA · On-site

$78K - $105K/yr

Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed ... What you bring * 3+ years of experience in MLOps, DevOps or platform engineering for ML or AI ...

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

Production Machine Learning Deployments * Model Monitoring, Observability, and Optimization ... Build Python-based tools and automation supporting ML operations and orchestration * Implement ...

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How much do machine learning operations jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for machine learning operations in the United States is $39.89, according to ZipRecruiter salary data. Most workers in this role earn between $33.41 and $42.31 per hour, depending on experience, location, and employer.

Is ML a high paying job?

Machine Learning Operations (MLOps) roles are generally well-paid due to the specialized skills required, such as expertise in cloud platforms, programming, and data management. Salaries tend to be higher than average tech roles and can increase with experience, certifications, and knowledge of tools like TensorFlow or Kubernetes.

What is the difference between Machine Learning Operations vs Data Scientist?

AspectMachine Learning OperationsData Scientist
Primary FocusDeploying, maintaining, and scaling ML models in productionAnalyzing data to develop insights and build models
Required SkillsML deployment, cloud platforms, automation, scriptingStatistical analysis, data visualization, programming (Python/R)
Work EnvironmentOperations teams, cloud infrastructure, production systemsResearch environments, data analysis teams, R&D
Common CertificationsCloud certifications, MLOps tools certificationsData science certifications, statistical courses

Machine Learning Operations and Data Scientists often collaborate, but MLOps focuses on deploying and maintaining models in production, while Data Scientists focus on analyzing data and developing models. Both roles require technical skills, but their day-to-day tasks and environments differ.

What engineer makes $500,000 a year?

Senior machine learning operations engineers with extensive experience, advanced skills in automation, cloud platforms, and deployment pipelines can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or large tech companies. Such roles often require expertise in tools like Kubernetes, Docker, and cloud services, along with strong problem-solving and leadership abilities.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers, AI research directors, or chief AI officers, often requiring advanced skills in machine learning, deep learning, and data science. These positions usually involve leadership responsibilities, extensive experience, and may include stock options or bonuses that contribute to the total compensation. Such roles are rare and highly competitive, often found in large tech companies or innovative startups.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI systems, and while AI automation tools can handle certain tasks, MLEs are essential for creating, tuning, and overseeing complex models. AI may automate some routine aspects, but MLEs' expertise in data engineering, model optimization, and deployment remains critical for effective AI solutions.
What cities are hiring for Machine Learning Operations jobs? Cities with the most Machine Learning Operations job openings:
What states have the most Machine Learning Operations jobs? States with the most job openings for Machine Learning Operations jobs include:
Machine Learning Operations jobs near you
Infographic showing various Machine Learning Operations job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $82,973 per year, or $39.9 per hour.
Senior Machine Learning Operations Engineer

Senior Machine Learning Operations Engineer

Garner Health

New York, NY

$256K - $285K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 21 days ago

Job description

About the role:

We are seeking a Senior MLOps Engineer to join our Platform Engineering team. This role will report to the Platform Engineering Manager, Developer Experience. As an early member of Garner's MLOps function, you will help build and operate the production machine learning systems that power our products, partnering closely with our machine learning and data science teams to enable the secure and consistent deployment of models. Given that these models directly influence health outcomes and cost-effectiveness for millions of patients, maintaining the highest standards of production quality is imperative.

Where you will work:

This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:
  • Help ensure the reliability, performance, functionality, and cost-efficiency of Garner's production ML systems, contributing to SLOs, observability, and on-call responsibilities.
  • Build key components of Garner's ML platform, including data infrastructure (such as a feature store, model registry, and CI/CD for models) and standardized service patterns.
  • Implement ML-specific CI/CD pipelines: Help transition our deployment process from manual notebook hand-offs to automated, PR-driven CI/CD workflows that include automated data quality checks and statistical model validation prior to deployment.
  • Drive down cost and latency through improved architecture, hardware choices, and model optimization as appropriate.
  • Contribute to the workflows, standards, and KPIs that support a growing MLOps function, helping teammates and stakeholders quickly identify the health of the team's products and focus on areas where issues reside.
  • Help establish drift monitoring: Design and implement automated data drift and concept drift monitoring systems that alert the team when models degrade, laying the groundwork for future Continuous Training (CT) architectures.
The ideal candidate has:
  • 5+ years of software engineering experience, with meaningful time spent operating ML or data-intensive systems in production.
  • Hands-on experience with the modern ML production stack: model serving (e.g., Sagemaker, Triton, or equivalent), feature stores, model registries, and CI/CD for ML.
  • Strong infrastructure and platform engineering fundamentals: Kubernetes, containerization, cloud (AWS preferred), Terraform/IaC, observability, and incident response.
  • Experience building ML platforms or significant components of one (not strictly consuming SaaS), with sound judgment around when to build vs. buy.
  • Strong collaboration with ML, data, platform engineers, data scientists, and product engineering teams, with the ability to lead projects and influence technical decisions.
  • Healthcare, regulated-data, or other high-stakes production ML experience is a plus but not required.
  • A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback
Technologies we use: 
  • Python, Kubernetes, AWS, Sagemaker, Terraform, S3, Snowflake, Airflow, Datadog

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $256,000 - $285,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more.

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