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Machine Learning Operations Jobs (NOW HIRING)

CGI Inc.

Machine Learning Operations Engineer

Dallas, TX · 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 ...

CGI

Machine Learning Operations Engineer

Dallas, TX · On-site

$113K - $136K/yr

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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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.
Machine Learning Operations Engineer

Machine Learning Operations Engineer

Associated Press

Manhattan, NY • On-site

$125K - $155K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 10 days ago

Job description

The Associated Press is an independent global news organization dedicated to factual reporting. Founded in 1846, AP today remains the most trusted source of fast, accurate, unbiased news in all formats and the essential provider of the technology and services vital to the news business. More than half the world's population sees AP journalism every day.
Why this role matters:
Partnering with Machine Learning Engineers, Data Scientists, and Platform Engineering, the Machine Learning Operations Engineer owns the production lifecycle of machine-learning systems at AP. This role is responsible for deploying, operating, scaling, monitoring, and governing ML workloads so they run reliably, securely, and cost-effectively in production.
The Machine Learning Operations Engineer ensures that models and inference pipelines built by ML Engineers can be safely promoted across Dev, QA, and Prod, meet operational SLAs, and evolve without introducing instability or uncontrolled cost.
This is an individual contributing production operations role, focused on runtime behavior, infrastructure, and reliability. It will report directly to our Director, Application Operations.
What you will do:
  • Design, deploy, and operate end-to-end production ML pipelines across Dev, QA, and Prod environments.
  • Set up and manage AWS SageMaker pipelines, endpoints, and monitoring for large scale inference workloads, including embedding generation, named entity recognition, reranking, and video processing.
  • Own GPU and CPU infrastructure selection, scaling, and optimization, including instance benchmarking, autoscaling behavior, and load testing.
  • Deploy, monitor, and operate inference services that support hundreds of thousands of queries per day across text, image, and video pipelines.
  • Establish standardized ML deployment patterns at AP, including:
    • Containerization and orchestration strategies
    • Environment isolation (Dev / QA / Prod)
    • Versioned promotion, rollback, and recovery mechanisms
  • Implement monitoring, alerting, drift detection, and evaluation metrics for production ML systems, tracking latency, error rates, throughput, and model/data drift.
  • Enable A/B testing and controlled rollout strategies for ML models in production, in partnership with engineering and product teams.
  • Partner closely with ML Engineers, Data Scientists, DevOps, and Platform teams to:
    • Operationalize new models and pipeline improvements
    • Promote systems across environments safely
    • Ensure deployments meet reliability, scale, and cost targets
  • Manage high-throughput I/O and data movement for large collections of media assets (text, images, video), avoiding CPU, network, and storage bottlenecks.
  • Reduce operational risk by enforcing reproducibility, observability, security, and cost controls across all production ML systems.

Who you are:
  • 5+ years of experience deploying and operating ML inference systems in production.
  • Strong experience with AWS SageMaker, including pipelines, endpoints, monitoring, and multi-environment deployments.
  • Expertise deploying ML models using PyTorch and TensorFlow from an operational and serving perspective.
  • Proven experience with model deployment and orchestration, including containerized inference and autoscaling.
  • Experience selecting, evaluating, and optimizing compute resources (GPU/CPU) for production ML workloads.
  • Experience setting up monitoring, evaluation metrics, and A/B testing frameworks for ML systems in production.
  • Ability to collaborate effectively with ML Engineers, Data Scientists, and platform teams in a shared ownership model.

What will set you apart:
  • Operational experience supporting ML systems involving:
    • Transformer-based NLP models (e.g., BERT-family models)
    • Computer vision models
    • Ranking and reranking systems
  • Familiarity operating systems that use common ML model types such as:
    • Convolutional and feed-forward neural networks
    • Ranking algorithms
    • Approximate Nearest Neighbor methods (e.g., HNSW)
  • Experience running ML workloads over large-scale text, image, and video datasets.

Why join us:
  • A mission-driven, inclusive environment focused on both individual and collective success.
  • Opportunities for professional development to help you reach your career goals.
  • Access to tools, mentorship, and resources tailored to elevate your proficiency and contributions.

Salary & Benefits:
The anticipated salary range for this position is $125,000 - $155,000, based on a candidate's skills, qualifications and location. The Associated Press offers comprehensive benefits, which include:
  • Competitive medical, dental and vision coverage
  • Retirement benefits
  • Company paid life insurance
  • Paid vacation and sick days
  • Paid parental leave for any new parent
  • Mental well-being resources

AP seeks to build an inclusive organization grounded in respect for differences. We support all aspects of diversity and provide equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, marital status, national origin, age, sexual orientation, gender identity, disability, status as a veteran, or other characteristic protected by law.

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