Top Machine Learning Jobs in Seattle, WA

The Staff Machine Learning Engineer will develop and productionize machine learning models, collaborate with product teams, ensure code quality, research emerging technologies, and share knowledge through presentations. They will focus on solving business challenges with AI-driven solutions and implementing machine learning systems.

As a Senior Machine Learning Engineering Manager, you will oversee the end-to-end machine learning lifecycle, mentor the team, research and implement advanced machine learning techniques, and collaborate with various stakeholders to enhance machine learning models and drive business impact.

The Machine Learning Engineer II will develop and optimize scalable machine learning models and data pipelines to enhance the Qualtrics platform. Responsibilities include collaborating with cross-functional teams, implementing new features, and conducting complex statistical analyses while ensuring the quality of end-to-end solutions.

The Threat Analyst role focuses on analyzing malware and threat detections, addressing customer inquiries, and improving detection capabilities. Responsibilities include reviewing product detections, managing false positives, and collaborating with internal teams to enhance threat detection processes using knowledge in malware analysis and machine learning.

The Senior Staff Scientist will lead the development of hybrid AI applications on quantum computers, collaborate across teams to optimize algorithms, and represent IonQ at conferences. The role requires expertise in quantum AI applications and significant experience in algorithm development and business problem-solving.

As a Machine Learning Engineer III, you will lead the development of machine learning models to enhance the shopping experience on Chewy's platform. This involves collaborating with leadership to improve product selection and advertising solutions, deploying innovative algorithms, and mentoring junior scientists while publishing research to advance industry knowledge.

You will develop machine learning models for fraud prevention, assess transaction risk, and work with engineering teams to implement robust scoring systems. Collaborate with data scientists to adapt to fraud trends, ensuring optimal model performance and feature integration.