Artificial Intelligence Jobs in Santa Clara, CA

Lead the vision and strategy for ServiceNow’s enterprise search, RAG, question-answering, and conversational AI products. Drive measurable business and customer outcomes, guide engineering and ML partners, incorporate user research and market insights, expand AI capabilities across the platform, and define performance metrics. Manage and mentor a team of at least four Product Managers while securing executive alignment and delivering scalable, customer-centric solutions.

Leads HackerOne’s partnership sales strategy, revenue performance, and team of seven Channel Account Managers. Builds and scales reseller, technology alliance, and cloud marketplace partnerships, including AWS. Establishes operating models, metrics, forecasting, enablement, and accountability while developing executive partner relationships. Collaborates with Sales, Marketing, Revenue Operations, Legal, Customer Success, and Product to drive partner-sourced and partner-influenced pipeline, revenue, and durable growth.

Lead launch and sustaining product marketing for large language and world models. Develop positioning, messaging, go-to-market plans, technical content, campaigns, and adoption programs. Translate AI research, benchmarks, architectures, and capabilities into accessible stories for researchers, developers, enterprises, partners, and media. Coordinate cross-functional launches, support communications and executive visibility, track competitive developments, and measure awareness, engagement, and adoption.

Leads complex, cross-functional supplier quality and IT programs across the global supply chain. Owns IT solution lifecycles, including requirements, development, deployment, training, adoption, and continuous improvement. Manages AI-enabled automation, predictive analytics, knowledge management, and SPC monitoring initiatives. Develops project plans, schedules, metrics, dashboards, and business processes while resolving risks and influencing stakeholders across organizational boundaries.

Leads Cisco CX AI engineering strategy and teams developing production-grade generative and agentic AI solutions. Responsibilities include architecting AI platforms, scaling data-driven systems, managing software and ML engineers, standardizing AI tools and infrastructure, partnering with product and business stakeholders, and establishing responsible AI, context engineering, and observability practices.

Develop and deploy foundational AI systems, including LLM inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish technical standards and governance, lead design reviews, contribute to long-term AI roadmaps, and mentor senior AI engineering staff.

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, scalability, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering talent.

Design, develop, deploy, and support foundational AI systems, including large language model inference, foundation model training, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Own complex system architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Build and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and orchestration pipelines. Lead cost-performance governance, improve model scalability and hardware utilization, shape technical roadmaps, establish AI engineering standards, and mentor senior technical staff. The role requires applying current AI research to reliable, responsible production systems across cloud and large-scale infrastructure.

Leads an AI engineering team developing, deploying, and supporting production AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, observability, and responsible AI governance. Owns technical strategy, optimization, build-versus-buy decisions, roadmaps, team development, and cross-functional collaboration. The role also applies emerging AI research to scalable, reliable, cost-effective production systems.

Conduct applied research and develop large-scale AI foundation models, particularly LLMs, optimization methods, and fine-tuning techniques. Responsibilities include designing, training, evaluating, validating, and deploying models; optimizing training and inference at scale; collaborating with data scientists, engineers, and product managers; translating research into business applications; and producing technical reports or conference publications.

Leads end-to-end strategy and operational roadmaps for complex HPC/AI products or portfolios. Defines value propositions, customer segments, business cases, market requirements, lifecycle strategies, and cross-functional alignment. Partners with engineering, supply chain, marketing, and other stakeholders to optimize product configuration, margins, pricing, market share, and go-to-market execution. Provides technical leadership, mentors teams, and drives innovation across ambiguous, high-impact initiatives.

Lead architecture and implementation of ServiceNow’s AI Control Tower, an enterprise platform for governing AI models, agents, and datasets. Drive products from 1.0 through customer adoption, establish scalable cloud-native and distributed systems foundations, guide AI-native engineering practices, mentor engineers, conduct architecture reviews, and coordinate with product, design, and partner engineering teams. The role requires hands-on technical leadership across SaaS architecture, data platforms, observability, reliability, security, and enterprise AI infrastructure.

Leads global pre-sales architecture and solution design for AI/ML networking, data center, hyperscale, and WAN opportunities. Translates customer needs into scalable end-to-end solutions, leads technical presentations, demonstrations, proof-of-concepts, RFP responses, and solution customization. Engages senior stakeholders and partners, develops architecture playbooks, influences technical strategy, mentors sales engineering teams, and drives successful complex deal execution. Travel of up to 50%, including international travel, is required.

Leads enterprise CRM voice AI deployments through customer pods, owning agent design, build, evaluation, production launch, monitoring, and optimization. Evaluates ASR and TTS models against production workloads, develops experimentation methodologies, guides platform decisions, and identifies repeatable industry-specific capabilities. The role combines technical product management, enterprise customer leadership, conversation design, speech model evaluation, telephony integration, and AI agent development.

Leads complex enterprise voice AI deployments, owning customer outcomes, agent design, evaluation, production launches, and optimization. Partners with engineering teams on conversational quality, real-time streaming, latency, orchestration, and voice pipeline decisions. Transforms customer-specific solutions into repeatable industry assets while shaping the broader product roadmap through hands-on exposure to production voice interactions and customer needs.

Own two enterprise customer pods through the full voice AI agent lifecycle, including design, build, evaluation, monitoring, optimization, and production launch. Manage customer relationships, timelines, and outcomes while partnering with forward-deployed engineers. Build scalable tooling for automated agent evaluation, call analytics, administration, and reliable agentic behavior. Convert customer learnings into platform roadmap improvements and repeatable productized solutions.

Leads a global team validating and publishing Juniper Validated Designs for AI/ML clusters, data centers, DCI, and campus networks. Owns solution validation roadmaps, NPI delivery, release quality, automation strategy, lab investments, team development, and customer escalations. Provides technical direction for EVPN-VXLAN fabrics, RoCEv2, congestion control, load balancing, and large-scale testbeds while partnering with product, engineering, sales, and customer teams.

Own the roadmap and execution for W&B Models, an experiment-tracking platform for AI researchers. Improve experiment organization, training visualization, monitoring, evaluations, and large-scale analysis across web and mobile. Conduct user research, translate ML workflows into product decisions, collaborate with engineering and design, and use data and feedback to launch polished, high-performing experiences. The role requires deep familiarity with ML research, technical product management, data visualization, AI tools, and developer or researcher audiences.

Leads budgeting, forecasting, long-range planning, financial modeling, performance reporting, SaaS metrics analysis, scenario planning, and process automation. Partners with executives and department leaders to drive data-informed decisions, profitability, and growth. Supports M&A and fundraising through due diligence and financial projections, while leading and mentoring the FP&A team and adopting AI tools to improve forecasting and financial processes.

Build and manage a global Afterpay and Cash App sales enablement and training strategy across North America, the UK, and APAC. Own the internal content platform, facilitate training, communicate product and business updates, collaborate with revenue and GTM leaders, and use AI to scale learning programs. Measure program impact on sales performance while adapting enablement initiatives to regional needs.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture, cost-performance optimization, technical standards, and design reviews for scalable production AI systems. Mentor senior technical staff and contribute to the long-term roadmap for responsible AI infrastructure.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, AI standards, and design reviews while mentoring senior technical staff. Partner with research, engineering, product, and program teams to establish foundational AI roadmaps and deliver scalable, responsible systems.

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research LLM pretraining, fine-tuning, reinforcement learning, robustness, explainability, and behavioral models. Develop platform or solution-level code, reproduce research, document experiments, and contribute to technical reports or conference submissions.