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

As of Aug 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.

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.

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.
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Machine Learning Operations Engineer jobs near you
Infographic showing various Machine Learning Operations Engineer job openings in the United States as of August 2026, with employment types broken down into 86% Full Time, 11% Part Time, 1% Temporary, and 2% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.

Machine Learning/Operations Research Engineer

Apple

Cupertino, CA • On-site

Full-time

Posted 17 days ago

Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers

Job description

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
The people here at Apple don't just create products - they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple and help us leave the world better than we found it.
Description
With the explosive growth of Apple products we are creating new opportunities for individuals to work on the most exciting new technologies at Apple. We are seeking a machine learning/operations research engineer to apply advanced mathematical modeling, statistical analysis, and optimization algorithms to solve complex manufacturing challenges. Machine learning/operations research engineers on our team directly impact our factory throughput, supply chain strategies, and cost-reduction initiatives by transforming raw operational data into actionable, data-driven decisions.
Minimum Qualifications
Master's degree or PhD in Operations Research, Industrial Engineering, Management Science, Applied Mathematics, or related field.
Proficiency with solvers and modeling languages such as Gurobi, CPLEX, CP-SAT, Pyomo, and GAMS.
Hands-on experience with simulation platforms like Arena, FlexSim, SimPy, and AnyLogic.
Experience with machine learning platforms such as PyTorch and Scikit-learn.
Excellent communication and presentation skills; ability to explain complex statistical and mathematical theories to non-technical stakeholders in simple, universal language.
Preferred Qualifications
Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning is a major plus.
Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc.
Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale is a plus.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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