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Machine Learning Operations Engineer Jobs (NOW HIRING)

Machine Learning & Operations Engineer

Miami, FL ยท Remote

$71K - $96K/yr

More typical DevOps responsibilities for software development as required. Requirements Required Qualifications * 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related ...

Machine Learning & Operations Engineer

Miami, FL ยท Remote

$66K - $89K/yr

More typical DevOps responsibilities for software development as required. Required Qualifications * 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related roles or relevant ...

Machine Learning & Operations Engineer

Arlington, VA ยท Remote

$80K - $108K/yr

More typical DevOps responsibilities for software development as required. Requirements Required Qualifications * 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related ...

Machine Learning & Operations Engineer

Miami, FL ยท Remote

$66K - $89K/yr

More typical DevOps responsibilities for software development as required. Requirements Required Qualifications * 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related ...

Machine Learning & Operations Engineer

Corvallis, OR ยท Remote

$72K - $97K/yr

More typical DevOps responsibilities for software development as required. Required Qualifications * 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related roles or relevant ...

Machine Learning Operations Engineer

Dallas, TX ยท On-site

$113K - $136K/yr

Machine Learning Operations Engineer Category: Software Development/ Engineering Main location: United States, Texas, Dallas Alternate Location(s): United States, Strongsville United States ...

Machine Learning Operations Engineer

Dallas, TX ยท On-site

$113K - $136K/yr

Machine Learning Operations Engineer Category: Software Development/ Engineering Main location: United States, Texas, Dallas Alternate Location(s): United States, Strongsville United States ...

This role focuses on operational excellence, including optimizing feature engineering pipelines and maintaining machine learning models in production environments. Desired candidate will work closely ...

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Machine Learning Operations Engineer information

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$36K

$85K

$135K

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

As of Jun 15, 2026, the average yearly pay for machine learning operations engineer in the United States is $85,029.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,500.00 and $94,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Operations Engineer, and why are they important?

To thrive as a Machine Learning Operations Engineer, you need a strong background in computer science, machine learning principles, and software engineering, typically with a bachelor's or master's degree in a related field. Familiarity with cloud platforms (like AWS, GCP, or Azure), containerization tools (such as Docker and Kubernetes), and CI/CD pipelines, as well as experience with MLOps frameworks (like MLflow or Kubeflow), is essential. Excellent problem-solving, collaboration, and communication skills help bridge the gap between data science and IT teams. These skills ensure efficient deployment, monitoring, and scaling of ML models, enabling reliable and maintainable AI solutions in production environments.

What is MLOps' salary?

The salary for a Machine Learning Operations (MLOps) Engineer typically ranges from $90,000 to $150,000 annually, depending on experience, location, and company size. Senior roles or those in high-demand regions can offer higher compensation, often including benefits related to cloud platforms and automation tools.

What engineers make $500,000?

Senior machine learning operations engineers with extensive experience, specialized skills in cloud platforms, automation, and large-scale deployment can reach or exceed $500,000 in total compensation, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires advanced certifications, leadership roles, and a strong track record in deploying scalable AI systems.

What is a ML operations engineer?

A Machine Learning Operations (MLOps) engineer is responsible for deploying, managing, and maintaining machine learning models in production environments. They work with tools like Docker, Kubernetes, and cloud platforms to ensure models are scalable, reliable, and integrated into applications, often collaborating with data scientists and software engineers.

How does a Machine Learning Operations Engineer typically collaborate with data scientists and software engineers on production projects?

Machine Learning Operations Engineers play a crucial role in bridging the gap between data scientists, who develop models, and software engineers, who deploy applications. They work closely with data scientists to understand the requirements and constraints of ML models, ensuring smooth transition from prototype to production. MLOps Engineers also collaborate with software engineers to integrate models into scalable, reliable systems while managing version control, monitoring, and continuous delivery pipelines. Effective communication and cross-functional teamwork are essential to address challenges like model drift, resource allocation, and deployment automation.

How much do machine learning ops engineers make?

Machine Learning Operations (MLOps) engineers typically earn between $90,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and automation tools can earn higher salaries, often exceeding $160,000 per year.

What is a Machine Learning Operations Engineer?

A Machine Learning Operations (MLOps) Engineer is a professional who specializes in deploying, managing, and maintaining machine learning models in production environments. They bridge the gap between data science and IT operations, ensuring that machine learning solutions are scalable, reliable, and efficient. MLOps Engineers automate workflows, monitor model performance, and address issues related to model versioning, data drift, and system integration. Their work is crucial for enabling organizations to leverage AI at scale while maintaining compliance and reliability.
More about Machine Learning Operations Engineer jobs
Infographic showing various Machine Learning Operations Engineer job openings in the United States as of June 2026, with employment types broken down into 77% Full Time, 11% Part Time, 3% Temporary, 6% Contract, and 3% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.
Machine Learning & Operations Engineer

Machine Learning & Operations Engineer

OptiTrack

Miami, FL โ€ข Remote

$71K - $96K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 22 days ago


Job description

OptiTrack is a global leader in motion capture technology, delivering precision tracking solutions for animation, robotics, virtual production, biomechanics, and industrial applications.

About the Role

OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning. This role sits at the intersection of machine learning engineering and infrastructure, focusing on automation of data validation pipelines, orchestration of large-scale experiments, and deployment of high-performance algorithms.

This is a fully remote position, working cross-functionally with research and engineering teams.

What Youโ€™ll Do

  • Design and maintain automated ML training pipelines.
  • Build infrastructure for large-scale distributed experimentation.
  • Develop CI/CD workflows tailored for machine learning systems.
  • Orchestrate data ingestion, preprocessing, validation, and model versioning.
  • Implement experiment tracking, hyperparameter tuning automation, and reproducibility systems.
  • Optimize GPU/compute utilization across cloud and on-prem environments.
  • Deploy, monitor, and maintain production ML models
  • Establish and enforce MLOps best practices including model registry, artifact management, and observability.
  • Improve system reliability, performance, and security.
  • Collaborate closely with ML researchers make new algorithms product ready.
  • More typical DevOps responsibilities for software development as required.

Requirements

Required Qualifications

  • 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related roles or relevant degree experience.
  • Experience with Python and ML frameworks (PyTorch, TensorFlow, or similar)
  • Experience building CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, etc.)
  • Hands-on experience with containerization (Docker) and orchestration
  • Experience managing GPU workloads and distributed training systems
  • Experience with cloud platforms (AWS, GCP, or Azure)
  • Strong understanding of automation, infrastructure reliability, and data pipelines
  • Ability to work with both European and US developers.

Preferred Qualifications

  • Experience with motion capture or computer vision systems
  • Familiarity with experiment tracking tools (MLflow, Weights & Biases, etc.)
  • Background in distributed systems or high-performance computing
  • Experience with workflow orchestration tools (Airflow, Argo, Prefect, Kubeflow)
  • Infrastructure as Code experience (Terraform, Pulumi, CloudFormation)
  • Experience with model optimization, inference acceleration, or edge deployment
  • Experience building tracking algorithms for device localization using techniques like SLAM
  • Strong problem-solving skills and attention to reproducibility
  • Comfortable working in a remote, collaborative environment, with international team members
  • Clear communicator who can bridge research and production engineering
  • Experience with image rendering pipelines in CryEngine.
  • Passion for building scalable AI infrastructure

Why Join OptiTrack?

  • Work on cutting-edge motion tracking systems
  • Contribute to technology used across robotics, animation, virtual reality, biomechanics, and virtual production
  • Remote-first flexibility
  • Opportunity to shape the next-generation of motion capture technology

Benefits

All benefits start on first day of employment!

  • 75% employer-paid medical for employee. Family coverage also included.
  • 100% employer paid dental, and vision for employee and dependents
  • 100% employer paid long-term, short-term disability, and life insurance policy
  • 401k Match, if youโ€™re contributing 5% we match 4%. 100% vested immediately.
  • 10 paid holidays
  • Starting at 15 days paid PTO (inclusive of sick and vacation time) annually
  • Employee Assistance Program (EAP)
  • Flexible Spending Account (FSA)

EEOC Statement:

OptiTrack is an equal opportunity employer, we believe in fostering a culture of equality, diversity, and inclusivity. Our commitment to this goal is clearly expressed in our zero-tolerance policy for discrimination and harassment of any kind, including on the basis of race, color, sex, age, religion, sexual orientation, national origin, disability, genetic information, pregnancy, protected veteran status or any other characteristic protected by applicable federal, state, or local laws. Our hiring practices ensure that decisions are based solely on qualifications, merit, and current business needs, while extending to all aspects of our operations - from recruitment and promotion to layoff and recall, to leave of absence, compensation, benefits, and training. We are committed to remaining a drug free workplace