Machine Learning Jobs in Glendale, CA

The Head of Product will define the product strategy, manage product teams, ensure quality user experiences, and collaborate cross-functionally to drive product outcomes, focusing on AI and user-generated content.

The DeFi Researcher will lead client engagements, perform research on DeFi mechanisms, develop predictive models, and publish insights while managing a team to ensure client success.

The Senior Architect will lead software engineering projects, mentor teams, and help clients modernize their technology stack using innovative software solutions and AI-driven capabilities.

The Design Technology Lead will utilize computational design to drive data-informed decisions, mentor teams, and foster community in design technology initiatives.

Lead the modeling strategy for ML Bots, developing predictive features, designing ML systems, mentoring engineers, and collaborating cross-discipline to enhance player experiences within games.

As Principal Software Engineer for the ML Platform team, you'll architect ML infrastructure supporting deployment, observability, and lifecycle management of models across Riot's games. Responsibilities include implementing CI/CD workflows, metric instrumentation, and ensuring a robust, extensible system for data scientists and product teams.

Lead the vision and strategy for ML Bots, ensuring integration with game teams, managing technical requirements, and overseeing product delivery in gaming environments.

The Product Manager will enhance Netflix's mobile personalization through algorithms, AI, and consumer insights, leading cross-functional teams to innovate user experiences.

This internship involves working with machine learning concepts, algorithms, and applications while collaborating on projects and preprocessing data.

The Data Engineer will build and scale data systems and machine learning models, collaborate with team members, and enhance data pipelines and quality systems for Netflix's Knowledge Graph.

As an ML Applications Engineer, you will design and maintain software solutions, implement data workflows, optimize applications, and collaborate on usability enhancements.

Lead and mentor the Generative AI team, developing and optimizing machine learning models and solutions for multimodal data processing and document understanding.

Design and deploy deep learning models for autonomous rail vehicles, focusing on machine learning and computer vision challenges. Collaborate with cross-functional teams to integrate ML into real-world applications.

Lead design and development of scalable ML infrastructure for autonomous vehicles. Collaborate with ML teams for efficient model training and deployment. Manage end-to-end ML pipelines and cloud-based solutions.

The intern will assist in developing AI and machine learning systems, gaining hands-on experience with data processing, model training, and evaluation techniques.

Design and optimize machine learning pipelines for large-scale systems, integrating models into user features and ensuring data integrity and performance.

Lead the design and optimization of ML systems at Scribd, managing cross-team projects, mentoring engineers, and shaping technical direction for ML initiatives.

Design and optimize machine learning systems, build scalable data pipelines, and run experiments to improve recommendations for Scribd's products.

The Staff Data Scientist will lead the design and development of Game Understanding Agents using machine learning methods, mentoring engineers and setting technical direction.

As an ML Engineering Intern, you will analyze stakeholder needs, research models, implement training loops, deploy models for texture generation, and evaluate model performance.

The Senior Machine Learning Engineer will design and develop ML models for 3D assets, integrating AI into creator tools while collaborating with engineering and creative teams.

Design and develop machine learning models and algorithms for advanced 3D printing systems, integrating with simulation frameworks and collaborating with software engineers.

The Staff Software Engineer will architect and implement ML infrastructure, focusing on deployment, monitoring, and lifecycle management of ML models, applying MLOps practices.

Lead and build a new ML Platform team, align stakeholders, architect infrastructure for ML inference at scale, and ensure team execution on product delivery.

The role involves designing, building, and optimizing large-scale ML systems for personalized recommendations, focusing on data ingestion, model training, and collaboration with cross-functional teams.