Artificial Intelligence Jobs in San Jose, CA

Design, build, and deploy LLM-powered tools, RAG-based knowledge systems, and agentic workflows to automate SOC integration tasks. Own reliability, evaluation, and observability for AI systems and collaborate with SOCD teams to scope, validate, and measure productivity gains from deployed automation.

Design, develop, and implement AI/ML models, manage AI solutions on cloud platforms, and collaborate with teams to optimize models.

The Sales Strategy and Operations Manager leads complex sales opportunities, develops ROI models, engages executives, and coordinates cross-functional input for compelling proposals.

The role involves managing training infrastructure, implementing distributed training algorithms, and collaborating with AI researchers on model training.

Lead the development of large-scale video foundation models for humanoid autonomy, focusing on training strategies and model evaluation for real-world applications.

Develop learning systems using reinforcement learning for autonomous robots, focusing on policy optimization and real-world application.

The Helix AI Engineer, Modeling will design core model architectures for AI systems, focusing on multimodal inputs and improving model capabilities for humanoid robotics.

The Helix AI Engineer will design, train, and deploy large-scale generative models, focusing on diffusion modeling for robots, while enhancing robot autonomy through perception and world modeling.

As a Helix AI Engineer focused on pretraining, you will design and train foundation models using multimodal data, improve generalization strategies, and optimize distributed training pipelines.

Build AI agents and architect product flows for NegotiateAI, focusing on agentic AI capabilities, user experience, and data handling requirements.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

As a Principal AI Engineer, you'll design scalable AI systems, mentor teams, develop large-scale LLMs, and optimize AI-driven cybersecurity solutions.

As a Staff AI Solutions Architect, you will design, implement, and deploy AI solutions for cybersecurity, ensuring performance and security in production environments while mentoring engineers and collaborating across teams.

As a Staff Software Engineer, you will design, develop, and enhance AI and machine learning applications and services, collaborating closely with cross-functional teams to ensure high-quality standards.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

As a Backend Engineer at Brain Co., you will design, build, and operate backend services and data pipelines for AI products, focusing on robustness and scalability. You'll collaborate across teams and own the full service lifecycle while ensuring high availability and performance. You should have 3+ years of experience in production backend systems and a strong foundation in APIs and distributed systems.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

As an AI Product Engineer, you will develop and deploy AI-driven software solutions, collaborate with customers, and optimize applications for real-world impacts.

Lead and architect AI initiatives, develop large-scale AI/ML systems, mentor teams, and drive engineering excellence across Altera.

The AI Application Engineer drives AI transformation in semiconductor test workflows, leading customer engagements and collaborating globally to deploy innovative AI solutions.

The AI Product Owner Leader will define the vision and roadmap for AI products, prioritize use cases, and coordinate with business and IT teams to deliver impactful AI solutions in global operations.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Drive benchmark results for GenAI inference, optimize performance across multiple models, and establish methodologies while leading technical initiatives in GPU performance engineering.

Lead technical performance analysis and optimization of large-scale AI/HPC clusters. Diagnose cross-component bottlenecks, build and maintain benchmarking suites, collect and transform telemetry into time-series dashboards, and collaborate with engineering, field teams, and customers to resolve configuration and performance issues.

As a Senior Applied AI and AI Infrastructure Engineer, you will manage AI projects, deploy infrastructure, mentor junior engineers, and apply AI for chip design.