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Machine Learning Infrastructure Engineer Jobs in Seattle, WA

Amperity

Lead Machine Learning Engineer

Seattle, WA · On-site

$116K - $153K/yr

Architect ML platform components-feature stores, model registries, and serving infrastructure-that ... Functional programming languages including Clojure and Python for ML pipelines. * Machine learning ...

IUNU

Applied Machine Learning Engineer

Seattle, WA · On-site

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.

Factspan Inc

Machine Learning Engineer

Seattle, WA

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

Factspan Inc

Machine Learning Engineer

Seattle, WA · On-site

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

Snapchat

Machine Learning Engineer, Level 4

Seattle, WA · On-site

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 ...

Amazon

Software Dev Engineer, EC2 Nitro

Seattle, WA

$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 ...

Amazon

Software Dev Engineer, EC2 Nitro

Seattle, WA

$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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All JobsMachine Learning Infrastructure Engineer JobsMachine Learning Infrastructure Engineer Jobs in Seattle, WA

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 Jun 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 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 Infrastructure Engineer jobs near you

Staff ML Systems Engineer, Distributed Systems

FieldAI

Seattle, WA

$195K - $230K/yr

Full-time

Posted 27 days ago

Job description

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.

We are seeking a Staff ML Systems Engineer to architect and build the distributed infrastructure that powers large-scale machine learning workflows across the organization.

This role sits at the intersection of machine learning, distributed systems, and platform engineering. You will be responsible for designing scalable systems that support data processing, model training, evaluation, and post-processing pipelines while enabling ML teams to efficiently develop, operate, and scale production-grade workflows.

You will play a critical role in defining the architectural patterns, tooling, and infrastructure that underpin our machine learning platform.

What You'll Get To Do
  • Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows.
  • Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms.
  • Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development.
  • Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability.
  • Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency.
  • Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development.
  • Establish best practices and engineering standards for distributed machine learning infrastructure.
  • Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs.
  • Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
What You Have
  • 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems.
  • Strong Python programming skills, including experience with concurrency, performance optimization, and systems development.
  • Experience with distributed computing frameworks such as Ray, Spark, Dask, Flink, or similar technologies.
  • Experience designing and scaling data pipelines or machine learning workflows.
  • Strong system design skills with demonstrated expertise in scalability, reliability, and performance optimization.
  • Experience diagnosing and resolving bottlenecks in distributed environments.
  • Ability to work cross-functionally and drive technical decisions across multiple teams.
The Extras That Set You Apart
  • Experience building infrastructure for machine learning training and inference systems.
  • Familiarity with modern ML frameworks such as PyTorch or TensorFlow.
  • Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies.
  • Experience operating Kubernetes-based infrastructure and large-scale cloud systems.
  • Deep understanding of distributed systems concepts including data locality, serialization costs, scheduling, and resource management.
  • Experience with distributed debugging, observability, and workflow orchestration platforms.
  • Proven ability to establish technical direction and influence architecture across organizations.
$195,000 - $230,000 a year

Our salary range is highly competitive with the market, but we take into consideration an individual's background and experience in determining final salary. Base pay offered may vary depending on geographic location, job-related knowledge, skills, and experience.

In addition to competitive compensation, FieldAI offers comprehensive benefits, equity participation, and the opportunity to contribute to cutting-edge advancements in AI and robotics.

Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.

Why Join FieldAI in Irvine?
In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.
You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.

Be Part of the Next Robotics Revolution
We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.

Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.

We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected statu
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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