Machine Learning Jobs in San Jose, CA

The Rapid Evaluator will identify and assess potential technology-enabled projects, lead teams, and develop business concepts for revolutionary technologies. Requires critical thinking and experience in breakthrough tech.

The role involves developing ML technologies using large language models, optimizing workflows, and collaborating with cross-functional teams to deliver impactful solutions.

The ML Compiler Engineer will enhance performance of neural networks, work with hardware architects, and implement compiler support for a custom architecture.

As a PhD intern, you will research generative models, collaborate on simulations, and contribute to high fidelity sensor data evaluation.

The candidate will design and optimize APIs and systems for ML workflows, focusing on scalability, operational rigor, and innovative solutions.

As a Machine Learning Infra Engineer, you'll build and maintain infrastructure for model training, deployment, and monitoring, improving workflows and ensuring data reliability.

Lead the development of machine learning models to enhance web security, analyze traffic, and mitigate attacks while collaborating with engineering teams.

Develop and optimize machine learning models, implement deep learning algorithms, and collaborate on scalable AI solutions, focusing on LLMs.

The Principal Product Manager will lead the AI Infrastructure team to develop AI/ML platforms, ensuring scalability, security, and alignment with business goals, while mentoring team members and driving innovation in AI technologies.

Design and implement models for spatial-temporal visual reasoning in autonomous driving, collaborating with ML engineers to optimize LLM/VLM performance.

The Machine Learning Engineer will build machine learning models and applications, drive technical design, and collaborate across teams to improve products and processes.

The Machine Learning Engineer will develop an online mapping system for autonomous driving using machine learning models, collaborating with teams to validate and deploy these models in vehicles.

The Machine Learning Scientist will innovate and optimize predictive models, influence content strategy, and manage the ML model development lifecycle. Strong communication and collaboration skills are essential.

Design, architect, and deploy AI systems. Develop production-grade software, lead technical initiatives, and ensure responsible AI development while mentoring teams in a fast-paced environment.

The role involves designing and architecting AI solutions on Snowflake's AI Data Cloud, collaborating with cross-functional teams, and influencing AI & ML roadmaps based on customer feedback. Responsibilities include driving POCs, publishing scalable content, and presenting technical strategies to stakeholders.

As a Staff Engineer, lead engineering teams, drive solutions for enterprise-class applications, mentor junior staff, and ensure high-quality product delivery.

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.

The role involves designing and developing machine learning infrastructure, enhancing core ML components, and collaborating with ML engineers on robust model pipelines.

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

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

As a Research Engineer for ML Systems, you'll optimize GPU clusters for AI, develop advanced technologies for consumer AI models, and improve model training and inference performance.

The role involves supporting ML infrastructure, building diagnostic tools, managing deployments, and optimizing GPU utilization for ML projects.

The Director of Machine Learning will lead the development of ML models for audio, vision, and language applications, mentor the team, ensure project milestones are met, and collaborate with different stakeholders.

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