Machine Learning Jobs in Santa Clara, CA

The role involves architecting GenAI systems, conducting experiments, analyzing data, and contributing to security solutions for enterprises.

The Machine Learning Engineer will write Python code, assist in experiments, support data processing, and engage with ML algorithms while learning from experienced engineers.

The Machine Learning Engineer will develop and test GenAI architectures, fine-tune LLMs, apply ML algorithms, and contribute to architectural design in a collaborative environment.

Design and develop advanced machine learning models for autonomous vehicles, integrating with safety standards and leading projects while collaborating across teams.

Develop and optimize machine learning models for Adobe's Document Cloud, implementing ML-Ops practices and collaborating with research teams.

The Senior Machine Learning Engineer will lead the design and deployment of large-scale ML systems, focusing on generative AI, model optimization, and collaboration with cross-functional teams.

Develop multimodal large language models for autonomous vehicles, optimizing performance and safety through advanced machine learning techniques and real-world data validation.

The role involves optimizing 3D rendering of sensor data using GenAI/ML, collaborating with teams to enhance sensor simulation realism, and ensuring high fidelity in synthetic data generation.

Develop advanced multimodal large language models for autonomous driving, optimize solutions using real-world scenarios, and collaborate with engineering teams.

The Machine Learning Engineer II will develop and maintain AI/ML systems, manage cloud operations, and collaborate across teams to deploy ML-powered tools.

Lead a team in developing AI/ML solutions within a data platform, focusing on innovation, technical excellence, and ethical practices while driving strategic growth.

Develop models and applications for Adobe's Digital Experience customers leveraging machine learning and predictive models, while ensuring best practices in ML-Ops.

Lead and scale machine learning efforts, manage teams, drive operational excellence, and align ML strategies with business goals.

As a Senior Generative AI Engineer, you will lead the development of models for synthetic data generation and enhance the safety and capability of autonomous driving solutions at Zoox.

Design and implement ML systems to model human-like driving behaviors, evaluate autonomous driving performance, and optimize software using AutoML techniques.

The role involves designing ML systems, developing evaluation methods for human-like driving behavior, optimizing software, and collaborating with cross-functional teams.

Design and optimize onboard software systems for deploying large deep learning models in production vehicles, enhancing performance and efficiency.

This role involves developing deep learning algorithms for autonomous driving, optimizing neural networks, and leveraging multimodal datasets for reinforcement learning and decision-making.

Design and improve end-to-end ML/AI pipelines while building high-quality production code and addressing new use cases. Collaborate with client data and enhance LLM training.

You will conduct model training, improve architectures, design experiments, and oversee team activities. A strong background in machine learning research is required.

Support training and benchmarking of LLMs, evaluate datasets, and assist the Pathway Research Team to advance AI capabilities.

Design and develop ML/DL algorithms and architectures for automated data analysis, build scalable ETL pipelines, and work collaboratively on projects.

The role involves designing ML models for autonomous driving, collaborating on stack requirements, and mentoring team members on high-quality code and ML practices.

Develop and support Machine Learning models for autonomous driving, collaborating across teams and ensuring high-quality experimentation and code, with a focus on cutting-edge AI solutions.

The role involves leading the development and deployment of machine learning applications in robotics, guiding a team, and defining technical strategy for AI projects.