Artificial Intelligence Jobs in Sunnyvale, CA

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 the strategy and development of next-generation post-training systems for Waymo’s autonomous driving AI. Advance post-training techniques, research applications for real-world autonomous driving, build scalable metrics, data, and flywheels, and collaborate with ML platform and onboard software teams. The role also requires influencing engineering strategy and stakeholders across the organization, including director and VP-level leaders.

Full-time hybrid summer technology internship at Johnson & Johnson from May 24 through August 13, 2027. Interns contribute to technology projects across software engineering, data engineering, AI and machine learning, cybersecurity, cloud and DevOps, UX, digital health, platform engineering, and connected devices. Responsibilities include building solutions, analyzing data and processes, collaborating with stakeholders, supporting project portfolios, and developing technology capabilities. The program may serve as a feeder into the Technology Leadership Development Program.

Develop mission planning, routing, and fault management algorithms for autonomous trucks. Interns will implement safety-critical robotics solutions, improve routing through construction zones, apply human operations commands, deploy code on real-world vehicles, and work with a production C++ codebase under mentorship from robotics engineers. The role provides hands-on experience with autonomous vehicle technology and potential full-time opportunities.

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.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

Design, build, and operate production-grade agentic AI systems: multi-agent orchestration, retrieval-grounded reasoning, model integration, observability and governance, and production search/RAG pipelines at enterprise scale.

Design, build, and operate production-grade agentic AI systems that reason over enterprise data, perform automated actions across workflows, and run safely at scale. Responsibilities include multi-agent orchestration, retrieval-grounding (RAG), model integration, observability, trust and governance, and technical leadership across architecture and delivery.

This role involves developing and implementing testing frameworks for ensuring reliability and performance of AI security controls across platforms. The engineer will expand test coverage and contribute to quality metrics for AI models.

Architects and delivers enterprise integration and API platforms using event-driven, asynchronous, cloud, and AI-augmented workflows. Develops reusable frameworks across SAP, SaaS, and partner ecosystems; governs APIs; integrates EDI, REST, GraphQL, and SOAP systems; and builds autonomous, self-monitoring and self-healing workflows. The role also requires programming, identity and access control expertise, CI/CD automation, observability, and close collaboration with Product and Engineering teams.

Owns AIX’s end-to-end learning strategy, including manager enablement, DRI and Atlas training, AI fluency, compliance training, and Aspire programming for early-career hires. Builds role-based curricula, learning content, champion and train-the-trainer networks, and measurement systems focused on behavior change, adoption, readiness, productivity, and data quality. Partners with HR, product teams, leaders, and subject matter experts across a distributed engineering organization.

Lead Databricks’ Learning initiative across self-paced and enterprise-managed learning products. Define technical and product strategy, own roadmap and delivery from 0-to-1 through large-scale adoption, establish engineering excellence, and partner cross-functionally with R&D, Learning, Marketing, and Field Engineering. Manage approximately 12 engineers while scaling the organization and building AI-powered learning systems, interactive labs, identity, sandboxing, provisioning, and distributed infrastructure for millions of learners.

Develop production AI-powered systems that improve software quality, engineering productivity, debugging, and developer workflows. Build intelligent services involving code analysis, retrieval, orchestration, evaluation, monitoring, and large-scale engineering data. Partner across teams to translate workflow challenges into reliable AI solutions, evaluate models and frameworks, and optimize quality, latency, cost, and reliability from concept through deployment and continuous improvement.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Lead design and development of AI/ML framework and inference software (ONNX, ONNX Runtime, Foundry Local). Provide technical leadership, mentor engineers, define requirements and design docs, drive performance, reliability, and observability improvements, act as DRI and participate in on-call operations, and collaborate across products to deliver scalable AI solutions.

Lead the architecture and development of Jerry.ai’s next-generation AI agents and workflow automation platform. Build conversational and computer-use agents, document validation, voicebot interactions, and integrations with external insurance systems. Evaluate emerging AI technologies, deploy production LLM systems, and improve reliability, latency, cost, evaluation, and failure handling. Partner with OpenAI on next-generation voice models and automation capabilities.

Lead the architecture and development of Jerry.ai’s next-generation AI and automation platform. Own conversational agents, voicebots, computer-use agents, backend workflow automation, document validation, transaction orchestration, and external insurance-system integrations. Evaluate emerging AI technologies, deploy production LLM systems, and partner with OpenAI on advanced voice and workflow solutions. The role requires end-to-end software ownership, strong architectural judgment, and experience managing evaluations, regressions, latency, cost, and failure handling.

Lead the architecture and production deployment of next-generation AI agents and workflow automation. Own conversational interfaces, voicebots, computer-use agents, document validation, transaction orchestration, and external system integrations. Evaluate emerging AI technologies, optimize LLM systems for reliability, latency, cost, and failure handling, and partner with OpenAI on advanced voice and automation models.

Owns AI rack- and cluster-scale hardware system architecture for Microsoft Azure infrastructure. Defines requirements and specifications, evaluates architecture tradeoffs across accelerators, networking, memory, storage, power, cooling, and datacenter constraints, and develops proof-of-concepts and performance models. The role drives cross-functional engineering alignment, influences industry standards and vendor roadmaps, and helps deliver scalable AI accelerator systems.

Lead the architecture and development of Jerry.ai’s next-generation AI agents and workflow automation platform. Own conversational interfaces, voicebots, computer-use agents, document validation, transaction orchestration, and integrations with external insurance systems. Evaluate emerging AI technologies, deploy production LLM systems, and partner with OpenAI on next-generation voice and automation capabilities. The role requires end-to-end software ownership, attention to evaluation, latency, cost, reliability, and failure handling.