Top Machine Learning Jobs

As a Sr. AI/ML Engineer II, you'll analyze datasets, build and maintain AI/ML models, and support technical architecture for classified systems. You'll interact with executives and provide expertise in security architecture.

The Machine Learning Engineer will manage the end-to-end ML lifecycle, collaborate with cross-functional teams, develop production models, and monitor their performance in high-stakes environments.

The Senior Software Engineer will design and implement ML systems, collaborate with cross-functional teams, and optimize ML deployment processes.

Eluvio seeks an AI-Machine Learning Intern to build ML models, collaborate on data science solutions, and enhance video/audio content intelligence.

The role involves building production-level machine learning models, optimizing algorithms for content delivery, and collaborating with teams to implement models. A focus on machine, deep learning, and data-driven insights is required.

Interns will collaborate with the machine learning and data science team to build models for video/audio content using large datasets and provide actionable insights.

As an ML Engineer, you will design and develop ML solutions, build infrastructure for testing, training, and deploying models, and contribute to enhancing coding agent capabilities at Sourcegraph.

Lead the design and architecture of GNN and transformer-based multi-entity embedding generation for Reddit's recommendation systems, collaborating across teams and optimizing machine learning pipelines.

Lead machine learning projects by defining the ML system development life cycle, mentoring teams, and integrating models for urban perception tasks.

The Machine Learning Engineer will develop and deploy AI systems for robotics, focusing on advanced manipulation techniques and control algorithms, collaborating across teams.

Manage the edge AI team to develop and implement machine learning models for edge devices, collaborating with stakeholders to optimize performance.

Design and implement machine learning models and systems for programmatic advertising, maintaining data pipelines and collaborating on product roadmaps.

Design machine learning models, maintain data pipelines, develop scalable model servers, and collaborate on product roadmaps.

The Machine Learning Solutions Architect designs and implements data solutions, overseeing model deployment and ensuring robust performance and integration within customer environments.

Develop and translate advanced ML models for heart disease diagnostics, collaborate with cross-functional teams, conduct research, and manage projects.

The Staff ML Scientist will develop innovative ML models for heart disease diagnosis and support regulatory submissions, collaborating with various teams and institutions.

As a Machine Learning Engineer at CompanyCam, you'll work on impactful ML projects, collaborate with cross-functional teams, and leverage Python and SQL for data-driven decisions.

Lead the Relevance and Personalization team to develop end-to-end machine learning systems, applying advanced algorithms across various data types to enhance search and recommendation functionalities at Airbnb.

The Sr. AI/ML Engineer III will analyze complex datasets, operationalize AI/ML models, and support leadership in a cleared environment, requiring extensive experience and technical expertise.

Develop prototypes for AI/ML applications, collaborate with mission stakeholders, and evaluate machine learning models for data integration and analysis.

The Machine Learning Software Engineer will develop workflows, integrate LLMs, enhance prompt engineering, and ensure efficient deployment for Experian Assistant.

As a Software Engineer for Machine Learning, you will develop systems for personalized user engagements at Tinder, utilizing ML techniques for optimization.

The Senior/Staff ML Engineer will lead AI/ML projects, focusing on LLMs, design scalable systems, mentor team members, and stay abreast of AI advancements.

The Founding ML Engineer will develop and deploy ML systems, collaborate with teams, and translate ambiguous problems into production ML solutions, shifting between client delivery and platform development.

Collaborate on designing recommendation systems, develop ML models, build infrastructure for deployment, conduct A/B tests, and ensure system reliability.