Artificial Intelligence Jobs in Sunnyvale, CA

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Design and build scalable AI platform capabilities to enable multi-step GenAI workflows, model execution, and integrations. Implement backend services, execution infrastructure, APIs, and observability to support production GenAI experiences across sales and product teams. Collaborate with AI engineers, data scientists, and product partners to turn AI use cases into reliable, extensible platform features.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Own strategic territories and close net-new enterprise software deals. Lead value-based sales cycles from discovery through proof of value, negotiation, and close; build executive relationships; develop account strategies; maintain Salesforce pipeline and forecasts; partner with alliances teams; and advise customers on supply chain resilience, risk, and compliance.

Supports the GTM Lead and founders across sales, growth, and content. Responsibilities include creating technical content for AI products, running growth experiments, supporting outbound initiatives, building the sales funnel, communicating with technical buyers, and collaborating with engineering in an early-stage environment.

Build Lingopal’s go-to-market motion for its evolving AI agent platform. Develop positioning, target accounts, outbound strategy, sales processes, and pricing inputs. Own enterprise sales from prospecting through discovery, pilots, and closing across customer-support-heavy enterprises and scaling AI-agent companies. Engage senior buyers including Chief AI Officers, COOs, and CTOs, while feeding market insights back into product strategy.

Build Weave’s AI-native lead generation infrastructure from scratch, including agentic workflows, automation, lead sourcing, enrichment, qualification, targeting models, and multichannel campaigns. Partner with account executives and RevOps to define lead quality, maintain HubSpot data hygiene, improve handoffs, and connect lead quality to pipeline conversion. Run data-driven experiments to optimize engagement, conversion rates, and pipeline contribution.

Lead editorial vision and content strategy for a technical AI research lab. Develop the editorial voice, calendar, content engine, long-form and short-form content, multimedia storytelling, and owned channels. Translate complex research into credible, accessible communications for technical and general audiences. Partner with engineers, researchers, communications, and product teams on launches and flagship reports; manage a social media manager, build a freelance writing team, and track content performance metrics.

Leads global sales and business development for an intellectual property portfolio, including strategy, licensing, partnerships, account development, negotiations, revenue growth, forecasting, and team leadership. Builds a distributed commercial organization and collaborates with engineering, legal, finance, product, and executives to monetize IP across semiconductor, AI, and hardware markets.

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.

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: 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.

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.

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.