Artificial Intelligence Jobs in Sunnyvale, CA

Design, build, and scale Databricks AI Runtime for large-scale GPU training. Lead architecture and implementation of multi-node orchestration, scheduling, checkpointing, resilience, throughput optimization, observability, and developer tooling. Mentor engineers, partner with product and research, and deliver high-impact production rollouts supporting thousands of accelerators and diverse model workloads.

Drive architecture and implementation of Databricks AI Runtime for large-scale GPU training. Build scheduling, distributed training performance, resilience, observability, and developer-facing APIs/CLI. Improve GPU utilization, fault tolerance, and multi-node orchestration. Lead cross-team initiatives, mentor engineers, and deliver high-impact platform features supporting thousands of accelerators and long-running jobs.

Lead hardware-software co-design to enable AI/HPC and agentic AI stacks on RISC-V CPUs. Integrate and optimize low-level kernel infrastructure, runtimes, distributed execution, memory systems, and developer tools, collaborating with architects, compiler engineers, and kernel developers to meet performance targets.

Lead backend architecture upgrades and implement AI-native infrastructure for high-QPS recommendation systems. Build safe-production AI agent infrastructure, automate engineering workflows, deliver backend features for product initiatives, and own reliability, observability, and safe rollout practices.

Design and architect Android systems and modular components to support multiple apps. Ship end-to-end user features using an AI-assisted workflow, improve developer tooling and CI, set quality and safety standards for AI-generated code, solve performance and production issues, and document playbooks for the team.

Lead development of the application and cognitive layer for humanoid robots: implement behavior trees and intent-to-action orchestration, build voice applications and multi-modal fusion, integrate robotics capabilities and edge/cloud AI services, and ensure production readiness with CI/CD, telemetry, and monitoring.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

Lead development of local AI agent frameworks and optimized runtimes on Windows for GeForce RTX PCs. Ensure secure, sandboxed execution and privacy-aware inference routing, collaborate with AI research and driver teams, mentor engineers, and produce production-ready code for autonomous, always-on desktop AI assistants.

Lead development of local AI agent frameworks and runtimes on Windows for GeForce RTX PCs. Optimize GPU-accelerated LLM inference, enforce policy-based privacy and sandboxing, collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Lead development of local AI agent frameworks and runtimes on Windows and GeForce RTX GPUs. Optimize agent inference, sandboxing, and privacy/security policies, collaborate with AI research and driver teams, mentor engineers, and deliver production-ready, local autonomous agent systems.

Own product vision and execution for Agent Opus, an AI-native multimodal video creation product. Translate AI research into delightful user experiences, prototype rapidly with engineers and designers, conduct user research, prioritize ruthlessly, and ensure high aesthetic quality for short-form video creation while driving product-market fit.

Architect and implement low-latency sensor-to-GPU data pipelines, develop system software, drivers, and APIs, work across FPGA, networking, and CUDA stacks, integrate diverse sensors into Holoscan, and debug and optimize real-time distributed systems under strict latency and throughput constraints while collaborating with hardware, firmware, and AI teams.

Lead architecture and build AI-native developer tooling and autonomous agent systems to accelerate engineering workflows. Drive platform standards for reliability, safety, observability, and cost-efficiency while partnering across teams and mentoring senior engineers.

Lead global product strategy and lifecycle for large-scale network orchestration, telemetry, and analytics products. Define technical use cases and architecture requirements, guide cross-functional teams, engage customers and partners, and deliver scalable datacenter networking solutions aligned with NVIDIAs AI infrastructure roadmap.

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.

Lead 0→1 product development for Emerald Cloud Services: define vision, conduct customer discovery, set product requirements for AI compute provisioning and management, partner with engineering and GTM teams, and launch and scale cloud services for enterprise and AI customers.

Build components of an AI Account Manager to automate hosting and optimization for advertiser accounts. Develop diagnosis and recommendation models using LLMs and delivery history, implement observability, dry-run safeguards, and evaluation pipelines, and collaborate with senior engineers and product to ensure reliable, auditable optimization that preserves account health and drives revenue.

Lead architecture and integration of RISC-V-based AI compute clusters and vector engines. Define ISA extensions, memory subsystems, interconnects, task-mapping and scheduling, and performance/debug features. Collaborate with compiler, ML runtime, RTL, verification, and firmware teams to optimize performance-per-watt and scalability across silicon generations.

Verify chip-level and performance behavior of custom ASICs: design end-to-end test flows with architecture team, run simulation-based verification, implement UVM environments, use constrained-random and functional-coverage methodologies, and coordinate vendors/contractors.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

The CX Manager will own the ERP integration domain, providing problem-solving expertise for complex customer issues and ensuring efficient issue resolution related to ERP systems and AI operations.

The Sales Strategy and Operations Manager leads complex sales opportunities, develops ROI models, engages executives, and coordinates cross-functional input for compelling proposals.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.