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Machine Learning Operations Engineer Jobs (NOW HIRING)

Apple

Machine Learning Engineer

Cupertino, CA · On-site

Bachelor's Degree in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field. Proficiency in one or more object-oriented programming languages such as ...

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Machine Learning Operations Engineer information

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

As of Jul 5, 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 June 2026, with employment types broken down into 78% Full Time, 20% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.
Machine Learning Operations Engineer II

Machine Learning Operations Engineer II

Kensho Technologies

Manhattan, NY • On-site

Full-time

Posted 2 days ago

Job description

Job Summary:
Kensho Technologies is S&P Global’s hub for AI innovation and transformation, focusing on machine learning and data discovery. They are seeking an MLOps Engineer to enhance ML processes and tools, empowering engineers to build robust and production-ready models and products.
Responsibilities:
• Iterate on Kensho’s ML processes to develop tools, services, and frameworks that make every stage of the ML workflow robust, auditable, and usable.
• Work closely with ML engineers to understand their unique processes, identify pain points, and form effective solutions.
• Empower engineers with the stable tooling necessary to rapidly experiment and actualize their research into demonstrable prototypes and mature products
• Provide resources and training for ML teams on best practices, enabling them to efficiently productionize their work to be leveraged by high-value products and services
• Evaluate, select and champion open source and third-party solutions, driving their adoption across teams and integrating into Kensho’s existing platform ecosystem
• Ship scalable, efficient, and automated processes for model fine-tuning and reinforcement learning and for the evaluation of LLMs/Agents
• Improve LLM and Agentic observability to help monitor agentic applications in production, detecting performance, decay and drift issues
• Stay at the frontier by actively tracking emerging tools and frameworks, promote best practices and strengthen the technical expertise of the team with your unique skill set
Qualifications:
Required:
• 2+ years of experience in ML infra, ML Ops, ML Engineering or some similar skillset
• Experience managing distributed systems with Kubernetes. It is important to understand Kubernetes concepts and trade-offs
• Cloud Platform (AWS) understanding. We utilize tools like EKS and managed ML services like Bedrock and SageMaker
• Python proficiency (we are a python shop mostly)
• Familiarity with distributed computing frameworks and workflow orchestration (ie. Ray, Airflow)
• Familiarity with software engineering best practices in an ML context
• Some basic understanding of ML concepts, LLMs and agents
• Ability to debug distributed systems across infrastructure, networking and application layers
• Excellent communication skills to drive adoption of new tools and best practices across multiple teams
• Someone who’s very curious, driven, low-ego and eager to learn across a range of engineering disciplines, while being part of a fantastic team
Preferred:
• Experience with Agentic AI systems, tools, frameworks and workflows
• Experience with running workflows on Ray
• Experience with MCP server patterns
Company:
Kensho is S&P Global’s hub for AI innovation and transformation. Founded in 2013, the company is headquartered in Cambridge, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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