Artificial Intelligence Jobs in San Jose, CA

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.

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.

The Principal Digital Design Engineer will architect and implement complex digital designs for connectivity solutions, oversee micro-architecture and RTL implementation, and mentor junior engineers.

The Risc-V Architect will analyze AI workloads, design custom RISC-V cores, and lead the architecture efforts at Tenstorrent, improving CPU performance.

Design and operate petabyte-scale end-to-end data pipelines for autonomous driving: onboard upload, cloud preprocessing, dataset production, and model training input. Build data cleaning, annotation QA, lineage, versioning, metadata management, and high-throughput distributed processing. Optimize I/O, memory, transmission, and containerized deployments; support cross-team collaboration and algorithm iteration.

Lead product strategy and roadmap for an enterprise agentic AI platform. Own end-to-end product lifecycle, gather requirements, write PRDs, run user research, coordinate cross-functional teams, ensure privacy/security compliance, define pilot metrics, and drive adoption of AI automation integrations with enterprise tools.

Develop and train large-scale multimodal foundation models (vision and language) that fuse imaging with molecular data, build scalable distributed ML pipelines for petabyte-scale datasets, and collaborate with experimental scientists and engineers to productionize models and systems.

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.

Lead applied research on spatial reasoning, geometric and physical world modeling. Own data generation, evaluation, and product integration. Implement experiments, build production code, and drive research strategy in 3D, multimodal, simulation, and related domains.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Lead customer engagements to define, develop, and deploy AI capabilities for the V93000 platform. Bridge semiconductor test engineering and AI to enable test program generation, debugging, root-cause analysis, and knowledge-driven workflows. Design and run demos, benchmarks, pilots, and training; collaborate with R&D and internal teams; drive product definition, feedback loops, and rollout/adoption of AI-assisted tools and workflows.

Lead and manage AI software product launches, collaborating with cross-functional teams and ensuring high-quality delivery while utilizing program management best practices.

Develop and test resilient, scalable multi-agent AI systems and RAG pipelines. Integrate agents with external tools and APIs, run LLM/RAG evaluations, write prompts, and support light model fine-tuning to improve network security product development efficiency.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

Evaluate and optimize NVIDIA AI infrastructure by running multi-node training/inference on large GPU clusters, designing benchmarks, profiling deep-learning workloads, identifying bottlenecks, producing tutorials and whitepapers, analyzing competitors, and presenting demos at major conferences.

Build and maintain end-to-end mobile trading systems for iOS and Android. Ship production-quality, well-tested code, apply blockchain and crypto knowledge, and integrate AI tools to improve engineering workflows. Break down projects, collaborate cross-functionally, conduct and act on code reviews, mentor teammates, and participate in hiring and onboarding. Influence product decisions through data-driven insights and provide domain expertise for mobile trading initiatives.

Design, deploy, and maintain on-prem AI infrastructure and agentic systems. Build GPU clusters, model serving, vector DB-backed RAG pipelines, fine-tune and deploy models, implement governance (MCP), and automate CI/CD and monitoring to integrate AI capabilities into enterprise workflows.

Lead and grow a hands-on AI Products engineering team building LLM and agent capabilities. Own technical vision, evaluation practices, and production delivery. Split time between management and coding, drive 0-to-1 product work, and ensure model quality, reliability, and upgrade pipelines.

Own and optimize on-board inference architecture for humanoid robots: map and partition models across heterogeneous accelerators, enforce compute budgets, optimize toolchains and kernels, apply quantization/pruning, profile pipelines, and collaborate with AI, platform, and silicon vendors to meet real-time power, latency, and reliability constraints.