1

Ml Infrastructure Jobs (NOW HIRING)

ML Infrastructure Engineer

San Francisco, CA · On-site +1

$126K - $166K/yr

They need compute infrastructure that stays out of their way: GPU access that's reliable ... Python-based ML and scientific computing tooling (PyTorch, JAX) * GCP and/or Modal experience

ML Infrastructure Engineer

San Francisco, CA · On-site

$220K - $250K/yr

They need compute infrastructure that stays out of their way: GPU access that's reliable ... Python-based ML and scientific computing tooling (PyTorch, JAX) * GCP and/or Modal experience

ML Infrastructure Engineer

Sunnyvale, CA · On-site

$119K - $187K/yr

Hands-on experience in ML platforms * Experience with GPU/TPU optimizations * Experience with Ray framework * Experience with Kubernetes at Scale * Experience infrastructure applications or similar ...

next page

Showing results 1-20

Ml Infrastructure information

See salary details

$46.5K

$127.1K

$182K

How much do ml infrastructure jobs pay per year?

As of Jun 8, 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:
Software Engineer, ML Infrastructure, Level 4

Software Engineer, ML Infrastructure, Level 4

Snapchat

Bellevue, WA

$195K - $231K/yr

Full-time

Medical

Posted 11 days ago


Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company's three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles.

Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.

You'll play a critical role in scaling our ML Infrastructure, optimizing training and inference systems, and driving innovations that make Snapchat's ranking and recommendation systems more efficient and impactful.

We're looking for a Software Engineer, ML Infrastructure to join Snap Inc!

What you'll do:

  • Design and optimize infrastructure systems for machine learning workloads at scale and drive reliability and efficiency improvements across Snapchat's ML Infrastructure

  • Build and enhance feature generation and serving pipelines that power online inferencing and offline training data generation

  • Develop high-performance inference systems to ensure fast and efficient AI model serving

  • Build infrastructure to perform scalable ML model training, evaluation, and inference in the cloud

  • Develop high-performance inference systems to ensure fast and efficient AI model serving

  • Build comprehensive data management systems for scalable data collection, labeling, processing, and evaluation

  • Work closely with ML engineers to deploy cutting-edge models into production

  • Utilize AI tools and high velocity engineering workflows to design and ship scalable services while upholding rigorous standards for code correctness, security, and production ready quality code

Knowledge, Skills & Abilities:

  • Strong programming skills in Python, Java, Scala or C++

  • Strong problem-solving skills with a focus on system performance, scalability, and efficiency

  • Good understanding of distributed systems and the infrastructure components of large-scale ML

  • Experience with big data processing frameworks such as Spark, Flink, or Ray

  • Ability to collaborate and work well with others

  • Proven track record of operating highly-available systems at significant scale

  • Ability to proactively learn new concepts and apply them at work

  • Adaptability in learning and applying evolving AI systems and tools to remain at the forefront of engineering trends and modern development practices

Minimum Qualifications:

  • Bachelor's degree in a technical field such as computer science or equivalent experience

  • 2+ years of post-Bachelor's software development experience; or Master's degree in a technical field + 1+ year of post-grad software development experience; or PhD in a relevant technical field

  • Experience building large scale production machine learning systems, distributed systems or big data processing

Preferred Qualifications:

  • Masters/PhD in a technical field such as computer science or equivalent industry experience

  • Experience working with ML Training platforms or optimizing AI model inference

  • Familiarity with ML frameworks such as TensorFlow, PyTorch, Caffe2, Spark ML, scikit-learn, or related frameworks

If you have a disability or special need that requires accommodation, please don't be shy and provide us some information.

"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).

Our Benefits: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!

Compensation

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

Zone A (CA, WA, NYC):

The base salary range for this position is $157,000-$235,000 annually.


Zone B:

The base salary range for this position is $149,000-$223,000 annually.

Zone C:

The base salary range for this position is $133,000-$200,000 annually.This position is eligible for equity in the form of RSUs.