Artificial Intelligence Jobs in Sunnyvale, CA

The VP of Product Marketing will lead strategy, messaging, and go-to-market initiatives while managing a high-performing team. Responsibilities include competitive intelligence, analyst relations, and sales enablement.

Lead strategy and execution for the Vault data & AI security product bundle, owning roadmap, cross-functional coordination, regulatory compliance, encryption, code signing, log export, and AI-native security features to scale monetizable, enterprise-grade security capabilities and drive adoption.

Design, train, evaluate, and productionize LLM-based NLU and agentic AI systems. Improve model quality, latency, reliability, and safety; fine-tune models, implement agent architectures, and collaborate across teams to deploy scalable, privacy-preserving conversational AI.

Design, build, and productionize agentic AI systems (agent orchestration, sandboxed execution, latency optimization, agent memory, LLM self-reflection, knowledge graphs, multimodal I/O). Ship scalable, production-grade code, mentor engineers, and collaborate with ML, security, product, and UX teams to deliver enterprise AI features.

Lead product management and program delivery for enterprise software implementations. Manage product lifecycle, stakeholder relationships, reporting, risk, and financial tracking; mentor juniors and align project objectives with strategic goals.

Use advanced analytics, statistical techniques, and machine learning to extract insights from large datasets. Design data solutions and pipelines, build predictive models (R, MATLAB), and apply TensorFlow and Scikit-Learn for deep learning and NLP. Collaborate with teams and clients, deliver quality work, and adhere to firm standards.

Lead global AI/ML engineering to define and execute model and platform strategy, build agentic AI and omni-channel AI experiences, scale ML infrastructure (training, feature stores, serving, MLOps), drive applied research (NLP, generative AI, RAG, RL), and recruit and grow a 100+ engineering organization to deliver enterprise-grade AI for Fortune 500 customers.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

As a Senior Software Engineer II, you'll design and operate a Kubernetes-native platform for AI workload orchestration, improve performance, and mentor engineers.

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.

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.

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.

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

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.

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.

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

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.

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

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

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

The Director of Digital Assurance & Transparency will lead AI solution teams, manage full-stack AI solutions, ensure quality, and foster innovation while driving business growth.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Lead design and delivery of production-grade AI capabilities to improve developer productivity across engineering tools, CI/CD, and embedded software workflows. Define technical vision, integrate AI into toolchains, drive productionization, establish responsible AI standards, mentor engineers, and influence cross-functional roadmaps and implementations.

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.

Lead research into vulnerabilities and defenses for LLMs and agentic systems. Perform deep-dive analysis of prompt injection, RAG pipelines, tool integrations, and autonomous agent security; map findings to industry frameworks; develop testing methodologies and publish thought leadership.