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Machine Learning Infrastructure Jobs (NOW HIRING)

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Machine Learning Engineer

San Francisco, CA · On-site +1

$196K/yr

Maintain and optimize machine learning infrastructure to support training, inference, and data processing workflows, including configuring cloud compute environments, tuning distributed computation ...

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

We are seeking an MLOps Engineer to build, deploy, and optimize machine learning infrastructure that supports scalable, secure, and production-ready AI solutions in cloud environments. Type

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

We are seeking an MLOps Engineer to build, deploy, and optimize machine learning infrastructure that supports scalable, secure, and production-ready AI solutions in cloud environments. Type

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How much do machine learning infrastructure jobs pay per hour?

As of Jul 7, 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.
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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 95% Full Time, 3% Part Time, and 2% 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 Engineer (Infra), Driver Understanding and Evaluation

Machine Learning Engineer (Infra), Driver Understanding and Evaluation

Waymo

Mountain View, CA • On-site

Other

Re-posted 21 days ago

Job description

The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack.

You will:

  • Build scalable systems for training and fine-tuning large-scale models to evaluate interesting driving behaviors.
  • Work at the intersection of data engineering, model development, and simulation Provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.
  • Contribute to the production and optimization of machine learning models aiming to assess Waymo's expansive fleet of vehicles that cumulatively travel millions of miles.
  • Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation.
  • Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.

You have:

  • M.S. or Ph.D. degree Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.
  • 3+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.
  • A history of contributions to machine learning tooling and frameworks e.g. PyTorch, Jax, Tensorflow, Ray, or similar. The candidate should understand both the user facing API and the internal workings. 
  • Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.

We prefer:

  • 5+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.
  • Experience in the autonomous vehicles domain, robotics, or complex simulation environments.
  • Familiarity with large-scale simulation platforms and their integration with ML training workflows.

Apply