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Machine Learning Infrastructure Jobs (NOW HIRING)

Talanto

$79K - $104K/yr

... infrastructure that powers our machine learning models. Your work will be instrumental in enhancing the scalability, efficiency, and performance of our AI-driven solutions. You will work with our ...

Compunnel

Machine Learning Engineer

Philadelphia, PA · On-site

... infrastructure. • Strong data analysis and statistical modeling skills. • Experience monitoring, maintaining, and improving production machine learning models. • Experience working with large ...

MatrixSpace

Machine Learning Engineer

Burlington, MA · Remote

$165K - $200K/yr

You'll also help build the ML infrastructure and tooling that accelerates future research, while ... Implement machine learning algorithms in high-performance C++ and Python with a focus on ...

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Machine Learning Infrastructure information

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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 ML Ops Engineer (Machine Learning Infrastructure)

Los Angeles, CA • Hybrid

Parallel Systems
Railroad Rolling Stock Manufacturing • 1 - 10 employees
Learn more about Parallel Systemsexternal

$116K - $158K/yr

Full-time

Re-posted 16 days ago

Job description

Senior ML Ops Engineer (Machine Learning Infrastructure)

Parallel Systems is seeking an experienced MLOps/ML Infrastructure Engineer to lead the design and development of the scalable systems that power our autonomy and perception pipelines. As we build the first fully autonomous, battery-electric rail vehicles, you will play a critical role in enabling the ML teams to develop, train, and deploy models efficiently and reliably in both R&D and real-world environments.

This is an opportunity to take full ownership of the ML infrastructure stack, from distributed training environments and experiment tracking to deployment and monitoring at scale. You'll collaborate closely with world-class engineers in autonomy, robotics, and software, helping shape the core systems that make real-time, safety-critical ML possible. If you're driven by building robust platforms that unlock innovation in AI and robotics, we'd love to work with you. 

This can be a hybrid role (minimum of 1 week per month onsite in Los Angeles) for a senior engineer with experience in 0 to 1 builds of perception systems. 

Responsibilities:

  • Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment and monitoring. 
  • Architect, deploy, and manage scalable ML infrastructure for distributed training and inference. 
  • Collaborate with ML engineers to gather requirements and develop strategies for data management, model development and deployment. 
  • Build and operate cloud-based systems (e.g., AWS, GCP) optimized for ML workloads in R&D, and production environments. 
  • Build scalable ML infrastructure to support continuous integration/deployment, experiment management, and governance of models and datasets. 
  • Support the automation of model evaluation, selection, and deployment workflows. 

What Success Looks Like: 

  • After 30 Days: You have developed a deep understanding of the product goals, existing infrastructure, and stakeholder requirements. You've conducted technical discovery and proposed a preliminary MLOps architecture-evaluating various ML tools, cloud services, and workflow strategies-clearly outlining pros and cons for each option. 
  • After 60 Days: You've delivered a detailed design document that outlines the end-to-end ML pipeline, including data ingestion, model training, deployment, and monitoring. Based on feedback from ML engineers and stakeholders, you've iterated on the design and built PoC for the core ML workflow aligned with the approved architecture. 
  • After 90 Days: You have delivered the core features of the MLOps pipeline and successfully integrated key tools (e.g., MLflow, SageMaker, or Kubeflow). You've also initiated the implementation of the remaining features, ensuring the infrastructure supports scalable, repeatable workflows for model experimentation and deployment in both R&D and production environments. 

Basic Requirements: 

  • Bachelor's or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline. 
  • 5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps. 
  • Proven experience architecting and deploying production-grade ML pipelines and platforms. 
  • Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment. 
  • Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar). 
  • Deep understanding of CI/CD practices applied to ML workflows. 
  • Proficiency in Python, Git, and system design with solid software engineering fundamentals. 
  • Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments. 

Preferred Qualifications: 

  • Experience with deep learning architectures (CNNs, RNNs, Transformers) or computer vision. 
  • Hands-on experience with distributed training tools (e.g., PyTorch DDP, Horovod, Ray). 
  • Background in real-time ML systems and batch inference, including CPU/GPU-aware orchestration. 
  • Previous work in autonomous vehicles, robotics, or other real-time ML-driven systems. 

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