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Machine Learning Infrastructure Jobs (NOW HIRING)

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Machine Learning Infrastructure information

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$15

$28

$52

How much do machine learning infrastructure jobs pay per hour?

As of Jun 14, 2026, the average hourly pay for machine learning infrastructure in the United States is $28.01, according to ZipRecruiter salary data. Most workers in this role earn between $21.88 and $30.29 per hour, depending on experience, location, and employer.

What is the difference between Machine Learning Infrastructure vs Data Engineer?

AspectMachine Learning InfrastructureData Engineer
Required CredentialsBachelor's in CS, experience with ML toolsBachelor's in CS, experience with data pipelines
Work EnvironmentFocus on ML systems, cloud platformsData pipelines, database management
Employer & Industry UsageTech companies, AI startupsAny industry with data needs, tech firms
Search & Comparison IntentUnderstanding ML system setupBuilding data pipelines

Machine Learning Infrastructure specialists focus on deploying and maintaining systems that support machine learning models, often working with cloud platforms and ML tools. Data Engineers build and manage data pipelines and databases, supporting data collection and processing. While both roles require technical skills and overlap in data handling, Machine Learning Infrastructure is more centered on ML system deployment, whereas Data Engineers focus on data architecture and pipelines.

What are the typical challenges faced by professionals working in Machine Learning Infrastructure roles?

Professionals in Machine Learning Infrastructure often encounter challenges related to scaling systems to handle large datasets, ensuring model reproducibility, and maintaining efficient workflows for both development and deployment. Collaborating closely with data scientists, software engineers, and DevOps teams is crucial to address issues like version control, resource allocation, and performance optimization. Staying updated on evolving tools and cloud platforms is also essential, as the landscape changes rapidly and impacts system design and integration.

What are the key skills and qualifications needed to thrive in Machine Learning Infrastructure, and why are they important?

To excel in Machine Learning Infrastructure, you need a solid background in computer science, software engineering, and distributed systems, often supported by experience in deploying and scaling machine learning models. Familiarity with cloud platforms (like AWS, GCP, or Azure), containerization tools (such as Docker and Kubernetes), and ML workflow systems (e.g., TensorFlow Extended, MLflow) is crucial. Strong problem-solving skills, collaboration, and the ability to communicate technical concepts effectively help you stand out in this field. These skills ensure scalable, reliable, and efficient deployment of ML solutions, enabling organizations to leverage machine learning at production scale.

What is machine learning infrastructure?

Machine learning infrastructure refers to the combination of hardware, software, platforms, and tools necessary to support the development, training, deployment, and maintenance of machine learning models at scale. This includes computing resources like GPUs and CPUs, data storage systems, workflow orchestration tools, model serving frameworks, and monitoring solutions. The goal of ML infrastructure is to streamline and automate the machine learning lifecycle, enabling data scientists and engineers to build and deploy models more efficiently and reliably.
More about Machine Learning Infrastructure jobs
What states have the most Machine Learning Infrastructure jobs? States with the most job openings for Machine Learning Infrastructure jobs include:
Manager, Machine Learning Infrastructure - SIML

Manager, Machine Learning Infrastructure - SIML

Apple

Cupertino, CA • On-site

$228K - $342K/yr

Full-time

Medical, Dental, Retirement

Posted 7 days ago


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

Do you think Computer Vision and Machine Learning can change the world? Do you think it can transform the way millions of people collect, discover and share the most special moments of their lives? We truly believe it can. And we are looking for hardworking engineers who can contribute to building the ecosystem of tooling necessary to create these exciting technologies.
We are the System Intelligent and Machine Learning (SIML) group that provides foundational computer vision and machine learning technologies to Apple's ecosystem. Our work is behind essential features such as Camera, Text & Handwriting recognition, and Apple Intelligence experiences (Image Playground, Writing Tools, Smart Script, Math Notes..). We are seeking an Engineering Manager to lead the development of scalable, high-performance infrastructure that powers product-focused machine learning initiatives.
Description
In this role you will lead a team responsible for building and operating infrastructure that enables large-scale data processing (terabytes and beyond) across domains such as image generation, large language models (LLMs), computer vision, natural language processing, human-computer interaction, and text recognition. This includes designing systems for dataset creation and management, ingesting annotated and inferred data, and delivering seamless access to high-quality data for ML researchers and engineers.
A key part of this role is driving systems that enable deeper understanding of model behavior-such as failure mode analysis, evaluation pipelines, and benchmarking frameworks-to accelerate iteration velocity and improve model quality. You will work across the stack, tackling challenges ranging from low-level distributed systems and compute efficiency to building stable, intuitive interfaces for internal users.
As a leader, you will partner closely with cross-functional teams including ML researchers, product teams, and platform engineering to define roadmaps, align priorities, and deliver impactful solutions. You will play a critical role in shaping how ML systems are developed, evaluated, and scaled from early experimentation to production.","responsibilities":"Lead and grow a team of engineers building ML infrastructure across data, training, and evaluation systems
Define technical strategy and roadmap for scalable ML data and systems infrastructure
Architect and develop systems for large-scale data ingestion, processing, indexing, and access
Drive best practices in system design, distributed computing, and performance optimization
Collaborate with cross-functional partners (ML researchers, product teams, infra teams) to deliver end-to-end ML solutions
Balance hands-on technical contributions with team leadership, mentorship, and execution
Improve developer experience through robust tooling, stable APIs, and self-serve workflows
Ensure systems are reliable, scalable, and efficient to support rapid iteration and production needs
Preferred Qualifications
Experience building infrastructure for ML workflows (data pipelines, training systems, evaluation frameworks, or deployment systems)
Domain experience in areas such as AI/ML, computer vision, NLP, or related fields
Experience working with large-scale datasets and compute-intensive systems
Experience improving developer productivity through tooling and platform abstractions
Ability to operate effectively in cross-functional, fast-paced environments with evolving requirements
Minimum Qualifications
Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, or a related field (or equivalent experience)
7+ years of software engineering experience, with 2+ years in a technical leadership or management role
Strong programming skills in one or more of: Python, Java, Go, C/C++
Solid understanding of machine learning fundamentals and ML system workflows
Proven experience in building and scaling distributed systems and backend infrastructure
Strong system design skills with expertise in at least one systems domain (e.g., data infrastructure, distributed systems, ML platforms)
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $228,100 and $342,800, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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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