Artificial Intelligence Jobs in Santa Clara, CA

The AI Infrastructure Product Marketing Manager leads the product marketing efforts for CoreWeave's AI cloud infrastructure, developing key narratives, messaging, and GTM strategies. Responsibilities include customer engagement, creating marketing materials, measuring impact, and collaborating with sales and engineering teams to drive adoption.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Manage AI engineering teams and client relationships, drive project deliverables, mentor staff, and uphold quality standards while leveraging advanced technologies.

Lead the creation of innovative AI solutions for healthcare systems, serve as a trusted advisor, foster collaboration, and drive operational excellence while managing complex challenges in healthcare AI.

As a Senior Manager in Data and Analytics Engineering at PwC, you'll design data architecture strategies for healthcare, mentor teams, influence stakeholders, and enhance healthcare operations through data solutions.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The Staff AI Engineer will build and deploy AI/ML solutions for simulations, working with teams to evaluate and implement AI tools. Responsibilities include leading projects, fine-tuning models, and optimizing performance across various applications in engineering.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Conduct applied research to design, train, evaluate, and deploy AI foundation models and recommendation/personalization systems. Collaborate with cross-functional teams to build models at scale using PyTorch, cloud infrastructure, and open-source tools. Publish research, deliver platform-level code, and drive long-term research agendas to improve customer experiences.

Optimize production AI inference on Arm-based edge devices by developing kernel-to-system level implementations, profiling and resolving performance bottlenecks, producing production-quality reference code and documentation, and collaborating with customers and internal teams to influence IP and tooling roadmaps.

Responsible for architecting and developing AI security infrastructure, optimizing performance and scalability of systems, and driving innovation in a collaborative environment.

As a Lead AI Engineer, you will design, develop, and support AI systems, collaborating with engineering and research teams to deploy scalable solutions that enhance customer interactions and improve banking services.

Lead strategy and execution for the Vault data & AI security product bundle (encryption, privacy, log export, code signing, AI Vault console). Define roadmap, drive cross-functional delivery, ensure regulatory compliance, enable customer adoption, and scale monetizable security offerings. Influence engineering, legal, platform security, and executive stakeholders to embed AI-native, automated security capabilities across the ServiceNow platform.

Design, implement, and operate Java and Python microservices for AI-native platform features running in Kubernetes. Build scalable APIs, distributed processing, integrate frontier AI SDKs, apply prompt engineering and observability, and lead architecture, code quality, and mentorship for production-grade GenAI services.

Design, build, and operate production-grade agentic AI systems that reason over enterprise data, execute workflows, and run safely at scale. Focus areas include multi-agent orchestration, retrieval-grounded reasoning (RAG), model integration, observability, governance, and technical leadership to ship reliable, low-latency autonomous agents in production.

Lead product for Integration Security and AI Gateway Security, defining roadmap and architecture for MCP governance, OAuth-based trust, credential mediation, access control, and audit. Partner with engineering, design, customers, and security teams to translate compliance and zero-trust requirements into developer-friendly features and drive cross-functional delivery and adoption.

Lead AI-focused product strategy and deliver features across a growing enterprise portfolio. Collaborate with engineering, customers, and cross-functional teams to gather requirements, build roadmaps, demo solutions, and drive customer-centric outcomes for Software Asset Management and licensing use cases.

Own and build the agent execution harness and orchestration runtime for enterprise-scale conversational/agentic AI: ensure reliability, observability, prompt/version management, LLM integration, eval frameworks, cost/latency optimization, and provide technical leadership for safe, production-grade agent workflows.

Lead and scale teams building ServiceNow's AI evaluation platform. Define strategy and roadmap for benchmarks, automated scoring, verifier systems, and quality monitoring. Deliver unified internal and customer-facing evaluation services with APIs and dashboards. Drive novel evaluation methods, safety assessment, and cross-functional collaboration to ensure reliable, production-grade AI quality.

Lead AI FinOps governance to define and track AI cost KPIs, detect anomalies, propose and implement cost optimizations, set spend guardrails with cloud/LLM providers, coordinate cross-functional governance, and present VP-level reporting.

Lead a team building evaluation infrastructure and benchmarks for agentic and generative AI. Design automated tests, validate RAG pipelines, benchmark frontier LLMs, and translate evaluation results into product and risk recommendations to ensure reliable, safe, and cost-effective autonomous workflows.

Support development and implementation of AI governance frameworks, assess governance/risk/controls, gather requirements, document processes, synthesize findings, advise on regulatory compliance, and help design scalable control programs for clients.