1

Machine Learning Infrastructure Engineer Jobs in Seattle, WA

Together, we power the platforms, AI-driven tools, live services, and infrastructure that ensure ... The Senior Machine Learning Engineer will report to the Senior Manager, EA Player Security Data ...

Machine Learning Engineer

Bellevue, WA · On-site +1

$117K - $152K/yr

We're looking for a Machine Learning Engineer to join our Offline Infrastructure team. This is an ideal role for a recent university graduate who is excited to work on large-scale systems and apply ...

Software Engineer, Systems ML

Bellevue, WA

$195K - $231K/yr

Meta is seeking a Software Engineer to join our Systems ML Engineering team, focused on building and optimizing the machine learning infrastructure that.

next page

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 Aug 25, 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 is a machine learning infrastructure engineer?

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 the key skills and qualifications needed to thrive as a machine learning infrastructure engineer?

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 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 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:

Infographic showing various Machine Learning Infrastructure Engineer job openings in Seattle, WA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $144,605 per year, or $69.5 per hour.

Machine Learning Infrastructure Engineer

Whatnot

Seattle, WA • On-site

$180 - $240/hr

Other

Medical, Dental, Vision, Retirement

This job post has expired today. Applications are no longer accepted.


Job description

Join the Future of Commerce with Whatnot!

Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.

As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.

We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.

Role

We're looking for builders- intellectually curious, highly entrepreneurial engineers eager to shape the future of AI and ML at Whatnot. You'll design and scale the core infrastructure that powers machine learning and self-hosted large language model applications across the company, working side by side with machine learning scientists to bring cutting-edge models into production and unlock entirely new product experiences. This means building systems that make advanced ML dependable and fast at scale-from low-latency, large model serving to distributed training & high-throughput GPU inference.

What you'll do:
  • Own the infrastructure powering AI and ML models across critical business surfaces-supporting growth, recommendations, trust and safety, fraud, seller tooling, and more.

  • Prototype, deploy, and productionalize novel ML architectures that directly shape user experience and marketplace dynamics.

  • Design and scale inference infrastructure capable of serving large models with low latency and high throughput.

  • Build distributed training and inference pipelines leveraging GPUs and both model and data parallelism.

  • Stretch beyond your comfort zone to take on new technical challenges as we scale AI across Whatnot's ecosystem.

US Based: We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.

You

People who do well at Whatnot tend to be comfortable figuring things out as they go, biased toward action, and genuinely curious about what they're building. They care more about outcomes than credit and stay close to the product and the people using it.

As our next AI/ML Platform Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus:

  • Bachelor's degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.

  • 3+ years of software engineering experience building and maintaining production systems for consumer-scale loads.

  • 1+ years of professional experience developing software in Python

  • Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams.

  • Experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis.

  • Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana.

  • Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink.

  • Professionalism around collaborating in a remote working environment and well tested, reproducible work.

  • Exceptional documentation and communication skills.

Benefits
  • Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)

  • Health Insurance options including Medical, Dental, Vision

  • Work From Home Support

    • Home office setup allowance

    • Monthly allowance for cell phone and internet

  • Care benefits

    • Monthly allowance for wellness

    • Annual allowance towards Childcare

    • Lifetime benefit for family planning, such as adoption or fertility expenses

  • Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally

  • Monthly allowance to dogfood the app

    • All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!).

  • Parental Leave

    • 16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.

EOE

Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.

#J-18808-Ljbffr