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Machine Learning Engineer Jobs in Seattle, WA (NOW HIRING)

Machine Learning Engineer As a Machine Learning Engineer on the AI Platform team, you will design and build the foundational infrastructure that powers Docusign's next generation of intelligent ...

Machine Learning Engineer (AI Data Trainer) About the Role What if your expertise in machine learning could directly influence how the next generation of AI models reason, plan, and solve complex ...

Applied Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and ...

Applied Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and ...

We're looking for an exceptional Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving ...

We're looking for an exceptional Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving ...

We're looking for an exceptional Machine Learning Engineer to help shape the future of our core platforms, products, and customer experiences. FinTech is one of the most complex and rapidly evolving ...

Machine Learning Engineer

Seattle, WA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build the foundational infrastructure that powers Docusign's next generation of intelligent systems. You ...

They are seeking a Machine Learning Engineer to develop AI-powered features that extract insights from structured and unstructured data, focusing on natural language processing and machine learning ...

They are seeking a highly-motivated, creative, and knowledgeable Machine Learning Engineer to help build a highly-scalable computational drug discovery platform. Responsibilities : • Develop and ...

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.

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at the ...

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at the ...

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

Machine Learning Role In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at the ...

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Machine Learning Engineer information

See Seattle, WA salary details

$35.8K

$146.5K

$220.2K

How much do machine learning engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for machine learning engineer in Seattle, WA is $146,543.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,500.00 and $176,400.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What jobs make $3,000 a month without a degree?

A Machine Learning Engineer typically requires a degree, but roles such as data annotator, technical support specialist, or freelance programmer can sometimes earn around $3,000 monthly without a formal degree, especially with relevant skills and experience. These jobs often involve self-taught skills, online certifications, or on-the-job training and may require proficiency in tools like Python or cloud platforms.
What are the most commonly searched types of Machine Learning Engineer jobs in Seattle, WA? The most popular types of Machine Learning Engineer jobs in Seattle, WA are:
What are popular job titles related to Machine Learning Engineer jobs in Seattle, WA? For Machine Learning Engineer jobs in Seattle, WA, the most frequently searched job titles are:
What cities near Seattle, WA are hiring for Machine Learning Engineer jobs? Cities near Seattle, WA with the most Machine Learning Engineer job openings:
Machine Learning Engineer

Machine Learning Engineer

DocuSign

Seattle, WA

Other

Medical, Retirement, PTO

Posted 10 days ago


Job description

Machine Learning Engineer

As a Machine Learning Engineer on the AI Platform team, you will design and build the foundational infrastructure that powers Docusign's next generation of intelligent systems. You will bridge the gap between core AI research and production-grade engineering, developing scalable platforms for autonomous agents, advanced retrieval systems, and automated model optimization. This position is an individual contributor role reporting to the Director, Machine Learning Engineering.

Responsibilities include:

  • Build and maintain high-performance distributed systems to support large-scale model inference and data processing
  • Design frameworks for multi-agent systems, focusing on state management, reliability, and long-running autonomous workflows
  • Architect sophisticated Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies to improve model accuracy and relevance
  • Develop platform-level tools for automated prompt engineering, evaluation, and optimization to accelerate the AI development lifecycle
  • Implement robust ML pipelines, focusing on observability, versioning, and the seamless deployment of generative AI services

Basic requirements include:

  • 5+ years of experience in machine learning engineering, software engineering, or related operational roles
  • Experience in software engineering with a focus on distributed systems and scalable backend architecture
  • Deep understanding of the ML lifecycle, from data ingestion and training to production monitoring
  • Experience building with LLMs, including RAG architectures and sophisticated prompt engineering
  • Experience deploying and maintaining ML models in high-traffic, production environments
  • Expertise in Python and experience with modern ML frameworks such as PyTorch

Preferred requirements include:

  • Experience with distributed task queues or stateful workflow engines for managing complex, multi-step AI processes
  • Experience with frameworks designed for horizontal scaling of compute-intensive ML workloads
  • Experience designing "agent-loop" architectures that involve tool-use, self-correction, and multi-step reasoning
  • Familiarity with vector storage systems and high-throughput data processing pipelines

Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience. Global benefits provide options for paid time off, paid parental leave, full health benefits plans, retirement plans, learning and development, and compassionate care leave.