ZipRecruiter

Log In

1

Ml Infrastructure Jobs (NOW HIRING)

Gridware

ML Infrastructure Engineer

San Francisco, CA

$190K - $260K/yr

Role Description As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure ...

Gridware

ML Infrastructure Engineer

San Francisco, CA

$245K - $260K/yr

Role Description As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure ...

techire ai

ML Infrastructure Engineer

San Francisco, CA · Hybrid

$250K - $350K/yr

We're hiring ML Infrastructure Engineers to tackle a hard, real-world problem, understanding what's happening on live job sites using wearable devices, large-scale video, and AI. This isn't clean ...

Gridware

ML Infrastructure Engineer

San Francisco, CA · On-site

$190K - $260K/yr

Role Description As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure ...

Spectral Labs

ML Infrastructure Engineer

San Francisco, CA

$126K - $166K/yr

ML Infrastructure Engineer Spectral Labs is a spatial intelligence company building reasoning models for engineering physical systems. Our model SGS-1 is state-of-the-art for parametric geometry, and ...

Sunday Inc

ML Infrastructure Engineer

Redwood City, CA · On-site

$131K - $172K/yr

Hands-on experience with ML training infrastructure, ideally PyTorch * Comfort reasoning about performance, memory, I/O, and GPU utilization * Experience managing training workloads (SLURM ...

Rebar

Senior ML Infrastructure Engineer

New York, NY · On-site

$118K - $161K/yr

We're looking for a Senior ML Infrastructure Engineer to build the platform our ML engineers depend on to rapidly iterate, experiment, and ship models - spanning feature pipelines, training ...

next page

Showing results 1-20

All JobsMl Infrastructure Jobs

Ml Infrastructure information

See salary details

$46.5K

$127.1K

$182K

How much do ml infrastructure jobs pay per year?

As of Jun 9, 2026, the average yearly pay for ml infrastructure in the United States is $127,066.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $141,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in ML Infrastructure roles?

Professionals in ML Infrastructure often encounter challenges related to scaling systems to handle large volumes of data, ensuring reliable deployment pipelines, and maintaining reproducibility across different environments. They must also collaborate closely with data scientists and engineers to streamline workflows and address issues like version control and model monitoring. Staying updated with rapidly evolving tools and best practices is essential, and balancing stability with innovation is a frequent aspect of the role.

What is the difference between Ml Infrastructure vs Data Engineer?

AspectML InfrastructureData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; knowledge of cloud platformsBachelor's in CS, Software Engineering, or related; experience with databases and ETL tools
Work EnvironmentFocus on deploying and maintaining ML systems, cloud environments, and infrastructure toolsDesigning, building, and managing data pipelines and storage solutions
Industry UsageUsed in AI/ML teams to support model deployment and scalabilityUsed across data-driven organizations for data management and analytics

ML Infrastructure specialists focus on deploying, scaling, and maintaining machine learning systems and infrastructure, while Data Engineers primarily build and manage data pipelines and storage solutions. Both roles require technical skills and often collaborate, but their core responsibilities differ in focus and tools used.

What are the key skills and qualifications needed to thrive as an ML Infrastructure Engineer, and why are they important?

To thrive as an ML Infrastructure Engineer, you need a strong background in software engineering, cloud computing, and machine learning concepts, often supported by a degree in computer science or a related field. Proficiency with containerization tools (like Docker and Kubernetes), cloud platforms (such as AWS, GCP, or Azure), and CI/CD systems is critical. Excellent problem-solving, collaboration, and communication skills help you efficiently work with data scientists and DevOps teams. These skills and qualities are vital for building scalable, reliable ML systems that support rapid experimentation and deployment in production environments.

What is ML Infrastructure?

ML Infrastructure refers to the underlying systems, tools, and processes that enable the development, deployment, and scaling of machine learning models. This includes data storage and management, computing resources, model training and serving environments, monitoring, and automation tools. ML Infrastructure ensures that data scientists and engineers can efficiently build, test, and maintain machine learning applications in a reliable and reproducible manner. It is a crucial foundation for organizations looking to operationalize AI and machine learning solutions at scale.
More about Ml Infrastructure jobs
What cities are hiring for Ml Infrastructure jobs? Cities with the most Ml Infrastructure job openings:
What states have the most Ml Infrastructure jobs? States with the most job openings for Ml Infrastructure jobs include:
What job categories do people searching Ml Infrastructure jobs look for? The top searched job categories for Ml Infrastructure jobs are:
Ml Infrastructure jobs near you
Infographic showing various Ml Infrastructure job openings in the United States as of June 2026, with employment types broken down into 1% Locum Tenens, 1% As Needed, 94% Full Time, 1% Part Time, 1% Temporary, and 2% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.
ML Infrastructure Engineer

ML Infrastructure Engineer

Gridware

San Francisco, CA

$190K - $260K/yr

Full-time

Medical, Dental, Vision

Posted 19 hours ago

Job description

About Gridware
Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware's advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.

Role Description
As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure around model deployment and monitoring. This role is essential to helping scale out the amount of time saving's Gridware brings to customers.
Responsibilities
  • Design, build, and maintain the infrastructure, tooling, and workflows that enable reliable, scalable deployment of ML models to production.
  • Develop monitoring and observability systems to track model performance, data drift, data quality, and overall system health.
  • Create and maintain end-to-end testing frameworks and simulation environments to validate models and pipelines prior to deployment.
  • Work closely with Data Engineering and Platform Engineering teams to ensure ML systems integrate cleanly with broader Gridware infrastructure and operational standards.
  • Improve CI/CD pipelines for ML workloads, ensuring reproducibility, safe rollout, and automated rollback strategies.
Required Skills
  • 5+ years of experience building production ML infrastructure
  • Strong software engineering skills and proficiency in Python
  • Experience with cloud platforms (AWS) and container orchestration (Kubernetes)
  • Familiarity with feature stores, model registries, or centralized metadata systems (i.e. MLFlow)
$190,000 - $260,000 a year
Senior ML Engineer Base Salary- $190,000-$210,000
 
Staff ML Engineer Base Salary- $245,000-$260,000.
**At this time, Gridware is unable to provide visa sponsorship or immigration support for this role. We're only able to consider candidates who are currently authorized to work in the country of employment without visa sponsorship now or in the future.**

This describes the ideal candidate; many of us have picked up this expertise along the way. Even if you meet only part of this list, we encourage you to apply!

Benefits
Health, Dental & Vision (Gold and Platinum with some providers plans fully covered) 
Paid parental leave 
Alternating day off (every other Monday)
"Off the Grid", a two week per year paid break for all employees. 
Commuter allowance 
Company-paid training 
apply for this job

Apply