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$15

$28

$52

How much do machine learning infrastructure jobs pay per hour?

As of Aug 20, 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 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 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 does a machine learning infrastructure engineer do?

A machine learning infrastructure engineer designs, builds, and maintains the systems and tools that support machine learning workflows, including data pipelines, model deployment, and scalable computing resources. They often work with cloud platforms, containerization, and automation tools to ensure efficient and reliable model training and deployment environments.
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What job categories do people searching Machine Learning Infrastructure jobs look for?

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Machine Learning Infrastructure jobs near you
Infographic showing various Machine Learning Infrastructure job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $58,269 per year, or $28 per hour.

Staff Machine Learning Infrastructure Engineer

Atoms

San Francisco, CA • On-site

$126K - $166K/yr

Full-time

Re-posted 19 days ago

Job description

Job Summary:
Atoms is building the machines that power the next era of progress. They are seeking a foundational Machine Learning Infrastructure Engineer to design and build the large-scale ML training infrastructure that powers their next-generation autonomous transport models.
Responsibilities:
• Design, implement, and scale repeatable machine learning infrastructure utilizing Kubernetes to support large-scale distributed GPU training of novel neural networks.
• Leverage distributed compute frameworks to efficiently manage and execute a high volume of complex ML training jobs concurrently across large GPU clusters.
• Integrate advanced model management and experiment tracking tools to provide researchers with deep observability into training metrics and run performance.
• Build and optimize high-throughput data ingestion pipelines to seamlessly stream petabyte-scale multi-sensor vehicle logs into training environments.
• Architect robust infrastructure for autonomous model validation and continuous integration testing, ensuring new vehicle policy releases are entirely regression-free.
• Partner closely with core robotics engineers and machine learning researchers to eliminate workflow bottlenecks and accelerate the deploy-to-vehicle lifecycle.
Qualifications:
Required:
• 8+ years of professional software engineering career experience
• Strong backend systems programming skills with proficiency in Go, Python, Java or similar
• Proficiency with Kubernetes for container orchestration and building cloud-agnostic environments from scratch
• Experience implementing distributed ML compute frameworks (e.g., Ray) to coordinate large pools of GPUs for heavy, multi-node workloads
• Hands-on experience building MLOps pipelines, metadata tracking architectures, and model registries using platforms like MLflow
• Prior experience managing high-throughput data pipelines using modern distributed data engines to feed data-hungry neural network architectures
Preferred:
• Familiarity or exposure to Rust considered a plus.
Company:
Atoms is a robotics startup that develops industrial robotics and physical AI systems to automate tasks across various industries. Founded in 2016, the company is headquartered in Los Angeles, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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