Top Machine Learning Engineer Jobs in Los Angeles, CA

Design and build a machine learning platform, deploy ML models, collaborate across teams, and establish monitoring standards for AI solutions.

The role involves designing, optimizing, and deploying machine learning models for edge environments, focusing on computer vision. The engineer will collaborate with teams to integrate models and improve inference efficiency while mentoring peers and upholding company values.

Design and develop edge AI firmware for camera systems, ensuring accurate AI algorithms, collaborating on architecture, and mentoring engineers.

Lead the design, development, and deployment of machine learning models, collaborating with teams and mentoring engineers. Focus on scalable ML workflows.

As a Senior Machine Learning Engineer, you'll lead the design, development, and implementation of ML models to detect risks like fraud and account takeovers, while mentoring junior team members and collaborating with stakeholders to translate business needs into technical solutions.

Lead the development of machine learning systems for fraud detection and risk modeling at scale, collaborating across teams and influencing ML strategy.

As a Senior Machine Learning Engineer, you'll design and deploy ML models to prevent fraud, enhance safety, and support financial services at Block.

The Machine Learning Engineer will design and implement ML algorithms for wireless communication, analyze RF datasets, and integrate ML with Silvus' products.

Interns will design, build, and deploy machine learning models that enhance Netflix's services, collaborating with various teams to translate business needs into ML solutions.

Assist in designing, developing, and deploying machine learning models for creating high-quality 3D assets while collaborating with cross-functional teams.

The Senior Machine Learning Platform Engineer will develop ML models, mentor engineers, optimize systems, and enhance ML infrastructure at Coinbase.

The Senior Machine Learning Platform Engineer will optimize ML pipelines, mentor junior engineers, and improve ML infrastructure, focusing on performance and data quality.

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

As a Machine Learning Engineer, you will develop and operationalize models for content understanding and discovery, collaborating with cross-functional teams and stakeholders to solve impactful problems using machine learning.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

The Senior Machine Learning Engineer will build AI-driven customer support products, collaborate cross-functionally, and maintain large-scale ML systems, leveraging modern techniques for real-world impact.

Lead the development of advanced machine learning models and systems, architect scalable solutions, mentor engineers, and drive improvement initiatives for product performance and innovation.

The Senior ML Engineer will design evaluations for AI features, optimize pipelines, and ensure quality in ML systems while collaborating with cross-functional teams.

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

Implement and optimize production-ready analytics code, maintain Python repositories, research data sources, build predictive models, and collaborate with teams.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

Lead the architecture and management of AI/ML infrastructure using Kubernetes and Terraform, optimizing hybrid environments for performance and scalability.