Artificial Intelligence Jobs in Santa Clara, CA

Lead a team to optimize algorithms for Deep Learning and Machine Learning, define technical initiatives, and collaborate with various teams for hardware and software innovation.

Design and implement an MLIR-based AI compiler for GPU inference: develop graph representations and dialects, optimize performance and memory, define APIs, generate kernels, and collaborate with framework and hardware teams to enable new model patterns and GPU features.

Lead product strategy and roadmap for an enterprise AI development platform enabling data ingestion, model fine‑tuning, orchestration, governance, and observability. Drive secure, compliant ML lifecycles and tooling so builders can train, deploy, monitor, and debug large-scale models and multi-step agentic workflows for global customers.

Support design, build, and deploy AI/ML models and AI-powered HR features (agents, chatbots, recommendations). Collaborate with HR, IT, and data teams, build HR data pipelines, document workflows, and handle sensitive HR data with confidentiality.

Lead design, build, and maintain scalable AI agents and backend systems for real-time, high-volume security operations. Integrate LLMs/GenAI, design data flows, APIs, databases, and enterprise authentication, collaborate cross-functionally, monitor and improve system reliability and compliance.

Support teams building AI workflows, automations, and agents; implement monitoring and anomaly detection; analyze and prepare operational data for model training; document workflows and update playbooks; test prototypes, evaluate performance, investigate issues, and track operational KPIs. Participate in learning, networking, and project showcases.

Lead technical programs with hyperscalers and OEM/ODMs to deliver AI datacenter infrastructure from design through production. Define schedules and milestones, translate customer requirements into cross-functional plans, resolve issues, manage qualification/validation, monitor factory production and yields, and communicate program health to customers and leadership. Champion post-deployment sustaining support and coordinate global teams to ensure timely delivery and customer acceptance.

Lead strategic technical programs with CSPs and ODM/OEM partners to deploy NVIDIA AI datacenter platforms. Integrate datacenter tools, operationalize AI factory processes, resolve blockers, manage timelines and cross-functional teams, ensure delivery of customer-specific features, and communicate program status and risks to senior leadership.

Lead architect and hands-on builder for AI solutions: evaluate feasibility and data readiness, translate business problems into technical and algorithmic requirements, design high-level architectures and prototypes, drive PoC to production handover, set MLOps/API standards, mentor team, and align North American implementations with European enterprise governance.

Optimize distributed training for 100B+ models across massive GPU fleets by writing and tuning custom kernels, improving IO/checkpointing/fault tolerance, building monitoring tooling, co-designing model and training recipes, and benchmarking heterogeneous accelerators and parallelism strategies.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.

The Software Engineer will develop backend systems for an AI Energy Agent, including data integration, microservices, and scalable architecture, focusing on performance and reliability.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Design, architect, and scale cloud backend infrastructure for real-time media and sensor data streaming. Own reliability and throughput SLAs, collaborate with AI teams, and mentor engineers.

The AI Research Scientist will develop and evaluate generative models for scientific data, collaborate with teams to apply AI in materials science, and mentor junior members while staying current in the field.

You will design and implement high-performance software while applying machine learning techniques to complex problems in electronic design automation.

As a Senior AI Systems Performance Engineer, optimize and scale foundation models, enhance performance across various system layers, and deliver high-performance AI applications.

Lead product strategy, roadmaps, and go-to-market for power semiconductor solutions targeting AI datacenter infrastructure and advanced power conversion. Drive market analysis, customer engagement, business planning, cross-functional commercialization, and thought leadership in SiC/GaN and next-generation power delivery architectures.

Develop physics-aware AI models and optimization workflows for semiconductor engineering. Design and train surrogate, operator-learning, and foundation models, build scalable data generation/training/deployment pipelines, collaborate with domain experts, and publish and drive adoption of Scientific AI solutions.

Own the inference performance product roadmap across the full stack, turning advanced LLM optimization techniques into widely adoptable platforms. Define strategies for agentic and multi-turn workloads, partner with open-source and internal teams to land optimizations, and own benchmarking, release readiness, regression tracking, and production feedback loops to improve latency, efficiency, and cost per token.

Lead technical strategy and execution for Merlin's AI Core platform across perception, sensor fusion, planning, and control. Build and manage a multidisciplinary engineering organization, own architecture from data pipelines to real-time onboard inference, enforce aviation-grade safety and quality, partner with certification and systems teams, recruit senior talent, and translate research into product opportunities.

Optimize large-scale distributed ML training and petabyte-scale offline inference for throughput and cost. Profile across stack, close performance gaps, improve multi-node scaling and cluster goodput, and build benchmarking and observability tooling while collaborating across teams.