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Ml Infrastructure Jobs (NOW HIRING)

AppLovin

ML Infrastructure Engineer

Palo Alto, CA · On-site

$126K - $165K/yr

The ML Infrastructure Engineer will design, develop, and maintain large-scale distributed systems while collaborating with various engineering teams to enhance the company's technology stack.

AppLovin

ML Infrastructure Engineer

Palo Alto, CA · On-site

$126K - $165K/yr

They are seeking an ML Infrastructure Engineer to design, develop, and maintain large-scale distributed systems while collaborating with various engineering teams to enhance their infrastructure and ...

Maven Robotics

ML Infrastructure Engineer

San Francisco, CA

$126K - $166K/yr

Build intuitive internal tools and abstractions that make complex infrastructure easy for engineers to use. * Lead technical and commercial discussions with cloud and ML compute providers, including ...

Cursor

Software Engineer, ML Infrastructure

Manhattan, NY · On-site

$120 - $150/hr

About the role The ML Infrastructure team builds large-scale compute, storage, and software infrastructure to support Cursor's work building the world's best agentic coding model. We're looking for ...

Zensors

AI/ML Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

The AI Infrastructure team at Zensors builds the engine that powers our visual sensing platform. We ... As a Machine Learning Engineer in ML Runtime & Optimization , you will develop technologies to ...

Arlo

Senior ML Infrastructure Engineer

New York, NY · On-site

$180K - $230K/yr

We're hiring a Senior ML Infrastructure Engineer to build and own the infrastructure that powers it -- from training models on tens of millions of patients and hundreds of millions of rows of claims ...

Maven Robotics

ML Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

Build intuitive internal tools and abstractions that make complex infrastructure easy for engineers to use. * Lead technical and commercial discussions with cloud and ML compute providers, including ...

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Ml Infrastructure information

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$46.5K

$127.1K

$182K

How much do ml infrastructure jobs pay per year?

As of Aug 23, 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 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.

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 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 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.

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Cities with the most Ml Infrastructure job openings:

What states have the most Ml Infrastructure jobs?

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Ml Infrastructure jobs near you
Infographic showing various Ml Infrastructure job openings in the United States as of August 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.

Data/ML Infrastructure Engineer

Matter Intelligence

San Francisco, CA • On-site

$126K - $166K/yr

Full-time

Medical, Dental, Vision

Re-posted 29 days ago

Job description

About the Role
We are seeking a Data Infrastructure Engineer to build and operate the infrastructure that turns drone, aerial, and orbital sensing data into production datasets, models, and customer-facing insights. This role spans ingestion, processing, storage, compute, and serving, with a strong emphasis on reliability, observability, performance, and cost.
You will work closely with research and product engineering to shorten iteration cycles, improve reproducibility, and raise the quality bar for production systems. You will define clear interfaces and operational standards that keep the platform trustworthy as data volume, model complexity, and product usage scale.
What You'll Do
  • Design, build, and operate scalable data and ML infrastructure on AWS, including workloads running on Kubernetes
  • Build and maintain systems for ingestion, processing, storage, and serving, with strong guarantees around data quality, correctness, and operational safety
  • Partner closely with research to support perception model training and evaluation workflows, enabling faster experimentation and more reproducible iteration
  • Build platform primitives for observability, data versioning, lineage, evaluation, reproducibility, and operational excellence
  • Partner with product engineering to ensure data- and model-derived insights are accessible through reliable, low-latency serving and retrieval interfaces
  • Design systems that enable efficient access patterns for customer-facing products, including search, indexing, and large-scale querying
  • Identify and address bottlenecks in throughput, cost, and operational complexity as the platform scales

What We're Looking For
You have strong software engineering fundamentals and have built production systems where reliability, cost, and performance matter. You can reason clearly about distributed systems tradeoffs, and you have experience designing data-intensive infrastructure that other engineers depend on.
You are comfortable working across data platform and ML platform concerns, and you understand how tightly coupled they become in production. You care about reproducibility, debuggability, and developer experience because you have seen how quickly they become bottlenecks.
You work effectively across research and product teams. You can translate ambiguous needs into clear interfaces and systems, and you can drive work from design through production while maintaining a high quality bar.
A few things we expect in this role:
  • Meaningful experience building production data infrastructure, ML infrastructure, or distributed systems
  • Strong programming skills in Python and SQL, with the judgment to choose the right abstractions and interfaces for production systems
  • Experience building and operating systems on AWS
  • Familiarity with modern infrastructure and platform tooling, including Kubernetes, Docker, and Terraform
  • Experience working with production storage and serving systems such as Postgres and Redis
  • Familiarity with data and ML workflow tooling such as Metaflow
  • Strong instincts for observability, testing, and operational excellence

Nice to Have
  • Experience supporting ML training, evaluation, batch inference, or model deployment in production
  • Familiarity with modern large-scale data patterns and tooling, including streaming, backfills, partitioning strategy, and schema evolution
  • Experience building internal platform primitives such as data versioning and lineage, dataset curation, experiment tracking, or tooling for reproducible workflows
  • Exposure to perception, multimodal, or geospatial systems, especially where data originates from real sensors and is used in real products

Location
This is a full-time role based in San Francisco, CA.
ITAR Requirements
To comply with U.S. export regulations, applicants must be one of the following:
  • A U.S. citizen or national
  • A lawful permanent resident (green card holder)
  • Eligible to obtain required authorizations from the U.S. Department of State

Employee Offerings & Benefits
At Matter, we believe in rewarding high performance and providing the support you need to thrive. Our compensation and benefits package includes:
  • Competitive compensation based on experience
  • Early-stage equity package
  • 100% employer-paid health, dental, and vision coverage
  • Opportunity to work on novel sensing, data, and AI systems with real-world deployment paths across drone, aerial, and orbital platforms

Who You Are
You are a strong engineer who likes building reliable systems that other teams can trust. You care about infrastructure quality, operational rigor, and clear interfaces. You are energized by working close to the data, close to the models, and close to the product.

Apply