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$15

$28

$52

How much do machine learning infrastructure jobs pay per hour?

As of Jul 29, 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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI systems, and their role involves understanding complex algorithms, data pipelines, and infrastructure. While AI automation tools can assist with certain tasks, MLEs are essential for building, optimizing, and overseeing AI models and infrastructure, making complete replacement unlikely in the near term.

How much do ML infra engineers make?

ML infrastructure engineers typically earn between $100,000 and $160,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and distributed systems can earn higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options in tech companies.

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 engineer makes $500,000 a year?

Senior machine learning engineers and infrastructure engineers with extensive experience, advanced skills in distributed systems, and expertise in tools like TensorFlow or PyTorch can earn salaries around or above $500,000 annually, especially in high-cost-of-living areas or large tech companies. These roles often require advanced degrees, certifications, and leadership responsibilities.

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 hardware, software, and tools needed to develop, train, deploy, and maintain machine learning models. It includes computing resources like GPUs and cloud platforms, as well as data storage, version control, and automation systems to support efficient model development and scaling.
More about Machine Learning Infrastructure jobs
What job categories do people searching Machine Learning Infrastructure jobs look for? The top searched job categories for Machine Learning Infrastructure jobs are:
Machine Learning Infrastructure jobs near you
Infographic showing various Machine Learning Infrastructure job openings in the United States as of July 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $58,269 per year, or $28 per hour.

Machine Learning Infrastructure Engineer

Bright Vision Technologies

Sunnyvale, CA • Remote

$100K - $150K/yr

Full-time

Posted 5 days ago

Job description

Machine Learning Infrastructure Engineer – Remote
Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Job Title: Machine Learning Infrastructure Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$150,000 Annually
Experience Required: 6+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary
We are seeking a Machine Learning Infrastructure Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads. The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.
Required Qualifications
  • Bachelor’s or Master’s degree in Computer Science or a related field.
  • Six or more years of experience in distributed systems, infrastructure, or ML platform engineering.
  • Strong proficiency in Python and a systems language such as Go, Rust, or C++.
  • Deep experience operating high-throughput, low-latency services in production.
  • Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM.
  • Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization.
  • Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
  • Experience with observability stacks including metrics, tracing, and structured logging.
  • Solid grounding in performance engineering and capacity planning.
  • Strong communication and incident response skills.
Preferred Qualifications
  • Open-source contributions to model serving infrastructure.
  • Experience with multi-region or globally distributed AI serving.
  • Familiarity with model quantization, distillation, and compression techniques.
  • Exposure to FinOps for AI workloads and cost-efficient serving design.
  • Experience supporting external-facing AI APIs at scale.
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to hilda@bvteck.com.
Bright Vision Technologies is an Equal Opportunity Employer.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees\' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

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