Artificial Intelligence Jobs in Sunnyvale, CA

Leads cross-functional AI Platform programs supporting autonomous vehicle verification, validation, reliability, and driverless readiness. Owns reliability metrics, execution plans, issue triage, testing priorities, risk assessments, and leadership updates across systems, software, hardware, autonomy, data science, and vehicle operations teams. Translates field failures into engineering priorities and improves troubleshooting, validation, and recovery workflows.

Designs and delivers AI-augmented enterprise integration platforms, API ecosystems, and autonomous workflows. Responsibilities include developing event-driven distributed systems, integrating SAP and SaaS applications, managing API lifecycles, supporting EDI deployments, implementing secure identity and access controls, and building CI/CD and observability capabilities. The role uses agentic AI, workflow automation, and intelligent monitoring to enable self-healing, adaptive integration across cloud and enterprise environments.

Develops strategic partnerships and enterprise opportunities for Intel-based Physical AI and robotics solutions. Builds adoption strategies, business cases, go-to-market plans, account plans, and co-sell motions with cloud providers, systems integrators, software partners, robot OEMs, and AI model providers. Positions Intel technologies, represents market needs internally, influences product roadmaps, and drives ecosystem-led deployment scale across industries.

Lead and grow an engineering team building enterprise-grade agentic AI integrations. Define technical strategy for multi-agent systems (MCP, A2A), collaborate cross-functionally, maintain highly reliable, low-latency services, and drive industry-spec contributions and interoperability.

Lead enterprise-scale AI transformation programs: design lighthouse pilots, translate strategy into roadmaps, establish governance and change management, mentor teams, drive cross-functional delivery, and codify playbooks for sustained adoption.

Leads engineering for an AI-native security operations product suite. Responsibilities include guiding architecture, setting engineering and product strategy, developing talent, engaging customers, and applying emerging AI innovations. The role requires leading high-velocity teams, shaping product roadmaps, coding with AI agents, building cloud software across AWS, GCP, and Azure, and collaborating with globally distributed teams.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.

Lead product marketing for CoreWeaves Agentic AI platform: define positioning, messaging, GTM programs, sales enablement, partner GTM, customer evidence, thought leadership, and market intelligence to drive adoption of agentic AI infrastructure.

Lead the design and deployment of AI agents, mentor engineering teams, and drive technical strategy and architecture for scalable AI systems.

Build a Physical AI security platform covering threat modeling, cyber risk analysis, intrusion detection, security testing, investigations, and agentic workflows. Develop full-stack features with TypeScript, React, and a typed backend language; integrate web applications, AI agents, and autonomous devices; deploy LLM-powered capabilities; and model relational domains with traceability and durable asynchronous workflows.

Designs and deploys end-to-end Physical AI and robotics solutions across data collection, simulation, model training, optimization, robotic hardware deployment, and fleet operations. Advises enterprise customers, cloud providers, systems integrators, and ecosystem partners on integrating AI workloads with IT, OT, edge, and cloud environments. Leads proofs of concept, technical evaluations, benchmarking, and solution validation while influencing Intel product roadmaps, strategy, and customer adoption.

Manage business operations for a developer ecosystem team by maintaining activation calendars, budgets, priorities, trackers, and roadmaps. Build AI agents, dashboards, scripts, and workflow automations to improve reporting and reduce manual coordination. Partner with team leads to identify risks, influence meetings and strategy reviews, establish scalable processes, communicate changes, and drive adoption of improved operating rhythms and tools.

Own a merchant-acquisition initiative for a home services platform, including market and territory research, self-sourced outreach, pipeline development, negotiation, onboarding, conversion analysis, business case creation, and weekly reporting. The intern will engage local service providers, track outreach metrics, develop partnership proposals, and present recommendations to leadership. This is a remote, outcome-focused internship requiring independent execution, quantitative analysis, and confident communication.

Leads AI and legal technology enablement across West Coast law firm offices. Designs training curricula, academies, workshops, communications, and adoption campaigns; manages change initiatives; builds attorney stakeholder relationships; analyzes usage data; reports progress and risks; and promotes responsible, habitual technology adoption among attorneys and practice groups.

Lead quality engineering activities for aerospace defense hardware, including developing QA systems, root-cause corrective actions, inspections, statistical analysis, audits, supplier corrective actions, and continuous improvement to ensure conformance to MIL-STDs and company/regulatory standards.

Lead regional enterprise sales for NVIDIA’s Physical AI and digital twin solutions. Build executive customer and partner relationships, develop end-to-end solution sales plays, enable global sales teams, manage lighthouse opportunities, track market adoption, and provide customer insights to product teams. The role focuses on industrial manufacturing, factory planning, robotics, synthetic data, intelligent infrastructure, and expansion across APAC and the Americas.

Recruit exceptional talent for Microsoft’s CoreAI division across systems engineering, AI science, and agent-native development. Partner with engineering and product leaders to develop hiring strategies, analyze talent data, build hiring plans, attract and close competitive candidates, and improve candidate experience. Advise stakeholders on talent gaps, influence hiring decisions, optimize recruiting processes, and maintain compliant candidate documentation under OFCCP and GDPR guidelines.

Design and deploy AI-driven, closed-loop laboratory automation systems for biological and chemical discovery. Integrate laboratory hardware, robotics, firmware, APIs, sensors, and cloud AI infrastructure. Build data pipelines for high-throughput genomic, proteomic, and imaging data used in machine learning. Conduct wet-lab experiments, prototype technical features, translate experimental constraints across biology, chemistry, hardware, and machine learning teams, and communicate complex results clearly.

Leads Wayve’s data strategy for autonomous-driving AI, including data acquisition, discovery, curation, enrichment, training, evaluation, deployment, and performance measurement. Builds the data flywheel, identifies rare and safety-critical scenarios, connects data investment to model outcomes, scales global data architecture, and leads a multidisciplinary Data organization. Partners with AI, Science, Validation, Product, Compute, Finance, and Commercial teams.

Lead enterprise-wide AI-enabled transformation programs: design lighthouse pilots, translate strategy into executable roadmaps, establish governance and change management, mentor teams, drive cross-functional delivery, and codify best practices into playbooks.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.

Lead the design, implementation, and deployment of scalable ML infrastructure for autonomous driving. Drive technical projects, mentor others, and collaborate with teams to enhance ML workflows.

Develops technical content, interactive demonstrations, reference architectures, and enablement tools that showcase AI infrastructure and data platforms. Partners with product, marketing, engineering, sales, and partners to translate complex AI/ML architectures, RAG workflows, storage, and networking technologies into compelling technical narratives. Presents at conferences and executive briefings, leads hands-on technical showcases, supports enterprise deals, and channels customer feedback into product and developer-experience improvements.

Lead AI engineering efforts from model development through production deployment. Optimize inference latency, throughput, GPU utilization, quantization, and serving costs; design evaluation systems and data flywheels using user feedback. Apply LLMs and multimodal models to video understanding, curation, ranking, personalization, and editing. Set technical roadmaps, review architecture, mentor engineers, manage a small AI team, and balance quality, reliability, latency, and infrastructure cost.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.