Machine Learning Jobs in Santa Clara, CA

Responsibilities include designing workflows, optimizing algorithms, developing Machine Learning solutions for EDA, and collaborating with cross-functional teams.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The role involves designing and developing machine learning infrastructure for annotation, evaluation, and training models, focusing on scalable systems and efficient data workflows.

The role involves developing privacy technology in machine learning, collaborating on modeling, and creating solutions for TikTok's product privacy requirements.

Design and build machine learning algorithms to optimize e-commerce search and improve overall platform matching capabilities, supporting TikTok's rapid e-commerce development.

This role involves full-cycle recruiting for technical talent in fast-paced environments, building partnerships, sourcing from unconventional channels, and improving recruiting systems.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Lead the development and deployment of advanced machine learning models focusing on NLP, while ensuring model integrity and communication with stakeholders.

Design and oversee the development of robust data platforms for training and evaluating ML models, focusing on autonomous driving. Collaborate with multiple teams and optimize data processing pipelines.

Develop and deploy machine learning algorithms for planning in autonomous trucks, ensuring safety and real-time efficiency.

This role involves improving AI and ML infrastructure for researchers by identifying bottlenecks, optimizing performance, and collaborating with diverse teams.

As an MLOps engineer, you will develop optimized data pipelines, enhance data engine architecture, and integrate foundation models while working closely with DoD customers.

As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.