Top Machine Learning Engineer Jobs in Los Angeles, CA

Lead the design and implementation of ML models for buyer personalization and feed recommendations. Oversee end-to-end ML projects and collaborate with backend engineers.

As a Machine Learning Engineer, you'll develop and deploy ML models to enhance user acquisition and engagement for Whatnot's Buyer Growth team.

Develop and productionize a next-gen Dispatch System, build predictive models, automate processes, and lead a small team in optimizing operations.

Responsible for designing, developing, and deploying ML models to optimize supply chain processes, collaborating with cross-functional teams, and mentoring junior scientists.

The Machine Learning Engineer will create models, evaluate technical tradeoffs, and ensure code quality, focusing on scalable products.

The Staff Machine Learning Engineer will lead technical strategy, collaborate on product development, ensure operational excellence, and foster team quality and ownership.

As a Machine Learning Engineer, you'll enhance ML productivity, improve model deployment, and implement algorithms for credit decisioning systems.

The Senior Machine Learning Engineer will enhance a platform for data scientists, lead ML service development, and collaborate cross-functionally to deliver AI solutions.

The Senior Data/ML Engineer will create scalable data/ML systems, maintain data products, and enhance business intelligence solutions through collaboration and ownership.

Lead machine learning strategy for Pinterest's search, focusing on indexing, query understanding, personalization, and generative approaches while mentoring junior engineers.

Lead the development of AI features using machine learning and large language models, mentoring the team, and transforming audit processes.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

As an ML Engineer, you will build and productionize predictive models for ad inventory and campaign performance, contributing to the growth of Netflix's ad platform.

The Senior Data Engineer will create scalable data pipelines and manage unstructured data for AI/ML workflows, ensuring data quality and supporting data solutions. Collaboration with cross-functional teams to meet business and security objectives is key.

The Machine Learning Engineer collaborates on improving the cloud-based ML environment, builds and deploys models, and enhances customer experiences through data.

As a Lead/Senior Machine Learning Engineer, you'll develop AI/ML solutions, collaborate across teams, and drive data-driven strategies to enhance product development and customer value.

The Principal Machine Learning Engineer will lead ML initiatives, develop models, and maintain ML infrastructure for Zynga's games while collaborating with data scientists.

As a Staff Machine Learning Engineer, you will develop scalable data infrastructure, manage ML workflows, and collaborate with stakeholders to optimize data usage and build automated processes.

Seeking a Machine Learning Engineer to enhance data capabilities and develop AI agents using LLMs, focusing on code data and smart contracts.

Lead the design and implementation of machine learning platforms at Yahoo, focusing on architecture, governance, and system performance while mentoring teams and influencing industry trends.

As a Senior Machine Learning Engineer, you will lead efforts in deploying ML applications, collaborate with teams to solve customer problems, and innovate AI applications.

Develop and maintain ML systems and pipelines, collaborate with teams, optimize model performance, and mentor junior staff to enhance user experiences in financial applications.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The ML Ops Engineer will deploy, optimize, and monitor machine learning infrastructure, collaborate with teams, and implement CI/CD pipelines.

Lead ML research and development, collaborating with engineers and clinicians to create enterprise-grade products and establish a research agenda while sharing insights through publications and presentations.