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Machine Learning Infrastructure Engineer Jobs in Seattle, WA

They are seeking an Applied Machine Learning Engineer to develop products for their clients and the greenhouse industry, focusing on creating machine learning models and retraining systems.

Proficient in JAVA & Python programming * Understanding of topic modelling, supervised & unsupervised machine learning * Plan the project milestones, resourcing and work distribution * Execute ...

Snap Engineering teams build fun and technically sophisticated products that reach hundreds of ... Experience working with machine learning, ranking infrastructures, and system design If you have a ...

New

Proficient in JAVA & Python programming * Understanding of topic modelling, supervised & unsupervised machine learning * Plan the project milestones, resourcing and work distribution * Execute ...

Integrate these models into the broader AI/ML infrastructure in collaboration with partner teams ... machine learning engineering, with a strong focus on AI/ML applications in insight generation ...

We're seeking an exceptional Software Development Engineer to build and optimize the performance ... This position offers the unique opportunity to shape the future of machine learning infrastructure ...

Software Dev Engineer, EC2 Nitro

Seattle, WA · On-site

$159K/yr

We're seeking an exceptional Software Development Engineer to build and optimize the performance ... This position offers the unique opportunity to shape the future of machine learning infrastructure ...

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Showing results 1-20

Machine Learning Infrastructure Engineer information

See Seattle, WA salary details

$52.9K

$144.6K

$207.1K

How much do machine learning infrastructure engineer jobs pay per year?

As of Jul 15, 2026, the average yearly pay for machine learning infrastructure engineer in Seattle, WA is $144,605.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,300.00 and $160,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Machine Learning Infrastructure Engineers, and how can these be addressed on the job?

Machine Learning Infrastructure Engineers often face challenges such as ensuring infrastructure scalability, managing resource allocation, and maintaining system reliability while supporting rapid experimentation by data science teams. Balancing the needs for flexibility in research environments with production-grade stability requires a deep understanding of both engineering best practices and the unique requirements of machine learning workflows. Collaboration with data scientists, clear communication about infrastructure capabilities, and staying current with fast-evolving technologies are key strategies for success. Most companies encourage ongoing learning and provide opportunities to contribute to architecture decisions, which makes this a rewarding environment for problem-solvers and innovators.

What are the key skills and qualifications needed to thrive in the Machine Learning Infrastructure Engineer position, and why are they important?

To thrive as a Machine Learning Infrastructure Engineer, you need a strong background in computer science, cloud computing, distributed systems, and experience with machine learning frameworks, often supported by a degree in a related field. Familiarity with tools such as Docker, Kubernetes, Terraform, as well as cloud platforms like AWS, GCP, or Azure, and certifications in cloud or DevOps technologies are highly valued. Strong problem-solving abilities, effective communication, and collaboration skills help engineers work seamlessly with data scientists and cross-functional teams. These skills are essential to design, implement, and maintain robust, scalable infrastructure that enables efficient machine learning development and deployment.

What is a Machine Learning Infrastructure Engineer job?

A Machine Learning Infrastructure Engineer designs, builds, and maintains the systems that support the development and deployment of machine learning models. This includes managing data pipelines, optimizing model training and inference, and ensuring scalability and reliability in production environments. They work closely with data scientists, ML engineers, and DevOps teams to create efficient workflows and infrastructure. Key technologies often include cloud platforms, containerization, orchestration tools, and distributed computing frameworks.

What are popular job titles related to Machine Learning Infrastructure Engineer jobs in Seattle, WA? For Machine Learning Infrastructure Engineer jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Infrastructure Engineer jobs in Seattle, WA look for? The top searched job categories for Machine Learning Infrastructure Engineer jobs in Seattle, WA are:
Machine Learning Engineer, Level 4

Machine Learning Engineer, Level 4

Snap, Inc.

Seattle, WA

Full-time

Medical

Re-posted 17 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 operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.


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.

We're looking for a Machine Learning Engineer to join Snap Inc!

What you'll do:

  • Build and deploy machine learning models that power core products, serving millions of Snapchatters

  • Apply modern ML techniques to solve large-scale, real-world problems

  • Own the full ML lifecycle from data analysis to production deployment

  • Partner with cross-functional teams to prototype and launch ML-driven features

  • 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 understanding of machine learning approaches and algorithms

  • Able to prioritize duties and work well on your own

  • Ability to work with both internal and external partners

  • Skilled at solving open ambiguous problems

  • Strong collaboration and mentorship skills

  • Proficiency in, or a strong aptitude for, leveraging AI tools to streamline development, paired with the critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks.

  • 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 relevant technical field such as computer science or equivalent years of practical work experience

  • 3+ years of post-Bachelor's machine learning experience; or Master's degree in a technical field + 2+ year of post-grad machine learning experience; or PhD in a relevant technical field

  • Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning

Preferred Qualifications:

  • Advanced degree in computer science or related field

  • Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks

  • Experience working with machine learning, ranking infrastructures, and system design

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 $173,000-$259,000 annually.


Zone B:

The base salary range for this position is $164,000-$246,000 annually.

Zone C:

The base salary range for this position is $147,000-$220,000 annually.This position is eligible for equity in the form of RSUs.