Artificial Intelligence Jobs in Sunnyvale, CA

Leads IT audit engagements assessing digital environments, processes, controls, cybersecurity, data integrity, and AI systems. Manages client relationships, audit teams, project delivery, compliance challenges, data quality validation, governance improvements, and risk management. The role also involves mentoring staff, applying IT governance frameworks, promoting innovative audit technologies, and ensuring assurance standards are met.

Leads the vision, strategy, roadmap, governance, and adoption of ServiceNow’s Lux AI-native experience platform. Oversees product management leaders and partners with executive stakeholders across Product, Engineering, Design, Research, and business units. Drives AI-first experiences, agentic workflows, contextual intelligence, reusable platform capabilities, enterprise scalability, security, privacy, accessibility, compliance, and responsible AI. Represents platform strategy with executives, customers, analysts, and strategic partners.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, quality engineers, and product owners to design, implement, test, and deliver scalable software. Apply high-performance algorithms, core AI/ML techniques, object-oriented programming, unit testing, profiling, and code tuning to improve user experiences and workflow efficiency.

Leads enterprise Total Rewards strategy, portfolio governance, planning, budgeting, communications, and execution. Coordinates initiatives across HR, Finance, Legal, Communications, and business planning teams; supports executive decision-making and acts as a proxy for the VP of Total Rewards. Drives AI-enabled transformation, process improvements, OKRs, change management, talent development, and special projects while ensuring alignment with broader organizational priorities.

Lead Developer Experience (DevExp) sales for Atlassian in AMER: execute go-to-market and territory plans, generate pipeline, manage full sales cycle from prospecting to close, build C-level relationships, collaborate with direct and channel teams, forecast revenue, and relay customer feedback to product and R&D to improve solutions.

Design and implement AI systems and scalable machine learning models, perform data wrangling and software engineering to deploy AI solutions, interpret data for client recommendations, collaborate across teams, uphold technical standards, and build client relationships while navigating complex, ambiguous problems.

Build and productionize reliable, scalable agentic AI systems (orchestration, sandboxed execution, latency optimization, agent memory, self-reflection, knowledge graphs, multimodal I/O). Implement frontier AI algorithms, ship production-grade code, mentor engineers, collaborate with ML, security, product, and UX teams, and drive roadmap and reliability improvements.

Design, evaluate, and productionize agentic AI systems and LLM-driven components to improve agent behavior, reliability, latency, and user-facing experiences. Build eval benchmarks, tune models, manage data quality, and collaborate with platform teams to deploy scalable, safe, and high-performing AI features.

Design and productionize agentic AI systems and components, improve agent behavior and reliability, create eval benchmarks and high-quality datasets, tune and evaluate LLMs and models, optimize latency and reliability in production, and partner with platform and UI teams to deliver end-to-end ML solutions.

Build and productionize reliable agentic AI systems: orchestration, sandboxed execution, latency optimization, agent memory, LLM self-reflection, simulated execution environments, and multimodal I/O. Collaborate across ML, security, product, and UX teams, mentor engineers, and ship production-grade code at scale.

Lead design and implementation of production agentic AI systems: agent orchestration, sandboxed execution, latency optimization, LLM-driven improvement, multimodal I/O, and platform reliability. Collaborate across ML, security, product, and UX teams, mentor engineers, and productionize frontier AI algorithms at scale.

Build and productionize agentic AI systems—agent orchestration, sandboxed execution, latency optimization, agent memory, LLM self-reflection, and multimodal I/O. Collaborate with ML, security, product, and UX teams, mentor peers, and deliver production-grade, scalable code to improve the Moveworks AI Assistant platform.

Build and productionize agentic AI systems: improve agent behavior, reliability, latency, and quality using LLMs, multimodal models, and vector databases. Design eval benchmarks, manage training and evaluation datasets, implement context engineering, orchestration, multilingual and multimodal support, and partner with platform teams to deliver production-grade, tested ML-driven features.

Design, build, and scale reliable agentic AI systems and infrastructure (orchestration, sandboxed execution, latency optimization, memory, multimodal I/O). Productionize frontier AI algorithms, collaborate with ML/security/product teams, mentor engineers, and deliver production-grade, performant code that advances enterprise AI assistant capabilities.

Principal Engineer responsible for driving the technical direction of ServiceNow’s conversational analytics products. The role builds cloud-based AI/ML solutions, develops prototypes, owns software delivery, and collaborates with engineering, product, and leadership. Responsibilities include designing agentic analytics capabilities, integrating semantic layers and knowledge graphs, improving accuracy through evaluation frameworks, and coaching engineers. Strong communication, customer focus, and expertise in large-scale software systems, AI-native development, analytics platforms, and natural-language querying are required.

Leads pricing and monetization strategy for ServiceNow’s AI and cybersecurity offerings. Responsibilities include market segmentation, packaging recommendations, cross-functional alignment with product and go-to-market teams, price realization and KPI reviews, pricing optimization, growth opportunity identification, and stakeholder management. The role also supports new pricing models, promotions, market launches, and execution planning.

Design and build cloud-based AI and machine learning solutions for ServiceNow’s conversational analytics products. Drive architecture and technical direction, create prototypes, collaborate with engineering and product teams, and own software through delivery. Develop agentic analytics capabilities involving semantic layers, knowledge graphs, data catalogs, and contextual engines. Coach engineers, communicate with technical and executive audiences, and prioritize customer outcomes such as accuracy and evaluation quality.

Leads the design, development, deployment, and governance of an enterprise AI platform on AWS and Kubernetes. Builds agentic and multi-agent workflows, secure MCP and tool integrations, identity-scoped authorization, policy controls, isolated container runtimes, audit trails, and observability. Partners with engineering, security, regulatory, product, and business teams to deliver compliant AI infrastructure, troubleshoot complex distributed systems, shape technology strategy, and mentor technical teams.

Lead the conversational core of ServiceNow’s Voice AI platform, owning dialog quality, LLM selection, evaluation, voice-to-voice model integration, deployment strategy, and enterprise customer enablement. Drive the product from early production validation toward approximately 100 live customers, improving conversational quality, containment, reliability, latency, transcription accuracy, and task completion. Define the roadmap, evaluations, and technical strategy for real-time voice interactions and scale the platform against major startups and CCaaS competitors.

Leads cross-portfolio AI product strategy at ServiceNow by combining customer research, technical product management, pricing, packaging, and commercial strategy. Partners with product, engineering, customer success, pricing, and go-to-market teams to identify adoption barriers, shape AI platform roadmaps, develop bundling and consumption-based models, and drive enterprise adoption. Influences independent organizations without direct authority and measures the impact of product and pricing changes on usage, expansion, and revenue.

Leads cybersecurity compliance and AI governance across NIST, CMMC, EAR, and ITAR frameworks. Maintains SSPs, POA&Ms, audit evidence, risk assessments, and compliance policies; evaluates AI vendors and use cases; embeds guardrails, access controls, data classification, logging, and review gates into LLM, MLOps, and DevSecOps workflows. Partners with engineering, security, legal, assessors, and government stakeholders to mature organizational compliance and responsible AI practices.

As a Research Product Manager, you will oversee complex research programs, turning technical ideas into execution plans and aligning research with production systems to enhance AI capabilities.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.