Machine Learning Jobs in San Jose, CA

As an Applied Scientist (ML), you will develop ML solutions, analyze existing systems, and create impactful ML prototypes and evaluation datasets.

Design, build, and improve ML systems and infrastructure, mentor junior engineers, and write maintainable code in a customer-focused environment.

Develop ultra-realistic simulations for autonomous vehicles using machine learning, focusing on realism and integration into production systems.

As a Machine Learning Intern, you will develop and implement ML algorithms, collaborate with engineers, and participate in hackathons to enhance the user experience at Tinder.

The Senior Software Engineer will develop algorithms for processing video and sensor data, improve ML models, and collaborate on backend systems and data infrastructure.

The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

As a Senior Staff ML Engineer, you will improve Coupang's search and discovery experience using advanced machine learning techniques, mentor junior engineers, and drive impactful projects.

The ML Infrastructure Engineer will design distributed systems for ML training, optimize inference, build automation pipelines, and monitor production performance.

Develop synthetic sensor simulation models and algorithms while collaborating with various teams to enhance sensor data and autonomy performance.

The role involves designing and maintaining ML and AI platforms, optimizing model training, ensuring reliability and scalability, and adopting new technologies.

As a Senior ML Infrastructure Engineer, you will design scalable architectures for ML, build robust data pipelines, manage GPU clusters, and drive improvements in CI/CD workflows while collaborating with cross-functional teams.

Design and develop core AI systems for intelligent data matching and recommendation engines. Work with advanced AI and cloud technologies to enable data-driven insights.

Develop computer vision and machine learning solutions, research algorithms, work with product teams to collect datasets, and balance research with production code.

Join Tenstorrent as a Senior Machine Learning Engineer responsible for developing AI application demonstrations and APIs. Experience in Kubernetes and AI frameworks is valued.

Design, build, and optimize high-performance network infrastructure for AI/ML operations, focusing on InfiniBand and Ethernet fabrics, while ensuring peak performance and network security.

You will build frameworks for LLM-powered agents, design reinforcement learning approaches, lead development of scalable evaluation systems, and mentor other engineers while writing robust code for enterprise-grade assistants.

The role involves designing and deploying risk models, enhancing credit and fraud models, and integrating advanced ML technologies. Responsibilities include collaboration, monitoring model health, and leading operational excellence.

Interns will work on manipulating and implementing computations on multi-dimensional tensors, develop software prototypes, and explore new designs.

This internship involves identifying performance bottlenecks in ML systems, improving developer experience, and applying optimization techniques in machine learning frameworks.

Lead the development and architectural design of AI and machine learning solutions, ensuring best practices in SDLC, model optimization, and team mentorship while managing end-to-end model lifecycle.

Develop scalable backend systems for AI applications in healthcare, collaborating on data pipelines, APIs, and ML workflows while optimizing performance.

The Machine Learning Scientist will design experiments, analyze AI model behaviors, and develop metrics to improve model evaluation, collaborating closely with engineers and product teams.

Lead open-source research in AI, design experiments, develop methodologies, and analyze data to enhance model evaluation and transparency.

Lead the design and development of scalable, high-performance real-time data and API infrastructure to support AI applications. Mentor engineers, ensure system reliability, and collaborate across teams.

The role involves co-designing hardware instructions and optimizing transformer model operations for AI chip performance, collaborating across software and hardware teams.