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Machine Learning Infrastructure Jobs (NOW HIRING)

Electronic Arts

Machine Learning Engineer III

Kirkland, WA · On-site

$122K - $158K/yr

Together, we power the platforms, AI-driven tools, live services, and infrastructure that ensure ... The Senior Machine Learning Engineer will report to the Senior Manager, EA Player Security Data ...

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How much do machine learning infrastructure jobs pay per hour?

As of Sep 12, 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 are popular job titles for Machine Learning Infrastructure?

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Machine Learning Infrastructure jobs near you
Infographic showing various Machine Learning Infrastructure job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $58,269 per year, or $28 per hour.

Senior Machine Learning Infrastructure Engineer

Santa Clara, CA • On-site

PlusAI
See more jobs at PlusAIexternal

$160K - $200K/yr

Full-time

Retirement

Re-posted 9 days ago

Job description

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World's Most Innovative Companies. Partners including TRATON GROUP's Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you're ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.
As a Senior ML Infrastructure Engineer at Plus, you will design scalable architectures capable of handling petabytes of data while ensuring optimal performance for both training and inference phases. You will build robust pipelines for managing model versioning systems and experiment tracking frameworks, which are essential for maintaining reproducibility across experiments. Additionally, you will be responsible for managing large-scale GPU clusters. This role offers unparalleled opportunities-both technically and professionally-for individuals passionate about solving challenging problems using modern cloud-native technologies. Ideal candidates thrive in environments that leverage tools such as Docker containers orchestrated via Kubernetes clusters, seamlessly integrated with state-of-the-art deep learning frameworks like PyTorch or TensorFlow. If you are eager to push the boundaries of what's possible in machine learning infrastructure and contribute to cutting-edge solutions, this position is an excellent fit!
Responsibilities:
  • Design and develop scalable, high-performance systems for training, inference, deploying, and monitoring ML models at scale.
  • Build and maintain efficient data pipelines, model versioning systems, and experiment tracking frameworks.
  • Collaborate with cross-functional teams, including ML researchers and engineers, to identify bottlenecks and improve platform usability.
  • Implement distributed systems and storage solutions optimized for machine learning workloadsDrive improvements in CI/CD workflows for ML models and infrastructure.
  • Ensure high availability and reliability of the ML platform by implementing robust monitoring, logging, and alerting systems.
  • Stay current with industry trends and integrate relevant tools and frameworks to enhance the platform.
  • Mentor junior engineers and contribute to a culture of technical excellence
  • Ensure that your work is performed in accordance with the company's Quality Management System (QMS) requirements and contribute to continuous improvement efforts.
  • Ensure team compliance with QMS, monitor quality, and drive process improvements.

Required Skills:
  • Phd or MS in Computer Science, Electrical Engineering, or related field
  • Good oral and written communication skills
  • Phd new grad or Masters with 3+ years of software engineering experience with a focus on ML infrastructure or distributed systems.
  • Proficiency in in Python, C++, SQL
  • Deep understanding of containerization, orchestration technologies, distributed ML workload, and experiment tracking tools (e.g., Docker, Kubernetes, multiprocessing, Kubeflow, and mlflow)
  • Deploy and manage resources across multiple cloud platforms (AWS, GCP, or on-prem environments)
  • Proficiency in at least one deep learning framework, such as PyTorch and data pipeline tools (e.g., Apache Airflow, Prefect).
  • Strong knowledge of distributed systems, databases, and storage solutions.
  • Extensive software design and development skills.
  • Ability to learn and adapt to new technologies and contribute in a productive environment.

Preferred Skills:
  • Familiarity with fundamental deep learning architectures, such as Convolutional Neural Networks (CNNs) and Transformer models
  • Experience in building large-scale ML datasets, MLOps pipelines, and distributed computing frameworks like Ray
  • Experience working with autonomous vehicles or robotics

Salary Range:
  • $160,000 - $200,000 a year

Our compensations (cash and equity) are determined based on the position, your location, qualifications, and experience.
Your opportunities joining PlusAI
Work, learn and grow in a highly future-oriented, innovative and dynamic field.
Wide range of opportunities for personal and professional development.
Catered free lunch, unlimited snacks and beverages.
Highly competitive salary and benefits package, including 401(k) plan.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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