Artificial Intelligence Jobs in Sunnyvale, CA

Design, build, and deploy production-grade agentic AI systems and scalable evaluation frameworks. Develop automated metrics to measure accuracy, safety, and reliability of non-deterministic agents. Automate and scale evaluation infrastructure to improve characterization and assessment of onboard autonomous driving software.

Build end-to-end Ambient AI products across the full software stack, ship rapidly, work with LLM-powered clinical documentation and billing systems, collaborate with customers, and ensure production reliability (alerting, metrics, SRE).

Build end-to-end Ambient AI products for healthcare, work across the software stack, and engage directly with customers to resolve issues.

The Director of Business Development will drive growth in the Physical AI sector by identifying market opportunities, managing strategic partnerships, and achieving revenue targets through effective sales strategies.

Design, build, and improve ML systems and infrastructure, mentor junior engineers, and write maintainable code in a customer-focused environment.

As a Staff AI Product Designer, you will lead the design of AI-driven user interfaces, collaborate with cross-functional teams, innovate user-centered designs, and ensure ethical considerations in AI applications across platforms.

Lead product definition and delivery for an AI compiler and runtime platform: own compiler/toolchain roadmap, framework integrations (PyTorch, JAX, vLLM, Hugging Face Optimum), kernel optimizations, scale-out orchestration (SLURM, Ray, LMCache), and developer UX to enable optimized execution on Majestic's Ignite APU.

Lead architecture and development of next-generation scale-up fabrics and collaborative communication for AI data-centers. Ensure scalability, reliability, and robust large-model training across hardware, software, and applications while driving innovation and cross-functional implementation toward production and manufacturing.

Lead and transform the US pitch and pursuit process by embedding AI enablement across stages. Manage a searchable global pitch content library, maintain structured experience and CRM data, create reusable pursuit assets and prompt frameworks, and develop training and adoption materials. Partner with BD, lawyers, and global teams to improve workflow, support high-priority pitches, and oversee directory and awards submissions to increase speed, quality, and consistency of wins.

Lead and conduct original research in embodied AI for autonomous systems, developing and evaluating large-scale perception, decision-making, and control models. Translate research into deployed prototypes, collaborate with engineering and field teams, support field tests and data collection, and define research strategy across reinforcement learning, vision-language-action, sim-to-real, memory, and multi-agent coordination.

Develop and deploy multimodal foundation models and agentic AI for multi-robot coordination. Design training recipes, curate datasets, build simulation and RL environments, evaluate performance, and integrate models into real-time robotic systems with field trials.

Partner with HR COEs to identify and prioritize AI use cases, evaluate and optimize Workday and AiMEE capabilities, lead pilot deployments, collaborate with data science and GIS teams to build HR analytics and AI solutions, and support planning, testing, and deployment to improve employee experience and process automation.

Lead HR requirements discovery and translate business objectives into Workday-aligned functional requirements and documentation. Liaise between HR COEs and GIS, support testing, release planning, deployment, and identify process improvements to enhance user experience and data integrity.

Research, design, and train foundation models for robotics; align text-based reasoning with low-level control policies; evaluate zero-shot generalization; run experiments; integrate models into robotic systems and present findings externally.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Design and architect multi-agent orchestration frameworks, build tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows and autonomous operations.

Run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tool use, and support RLHF alignment and constitutional AI development to ensure safe AI deployment.

Lead product strategy and roadmap for AI and robotics products, translating VLA and agentic RAG breakthroughs into customer value, defining safety/usability/performance metrics, working with enterprise adopters, and driving cross-functional delivery.

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.

Optimize, compress, and distill large language and vision models for on-device inference. Build pipelines for distillation and hardware-specific compilation, and benchmark performance across NPU/GPU architectures.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Develop and deploy embodied AI on humanoid robots: collect teleoperation data, train RL/imitation policies, integrate perception and control, optimize low-latency inference, and iterate experiments in real-world environments using simulation and scalable training pipelines.

Build and maintain backend and data pipelines powering ads, personalization, and retention systems. Contribute to recommendation algorithms, feature pipelines, experimentation infrastructure, revenue operations tooling (commission/quota), and observability for large-scale user and engagement data. Collaborate with product, sales, and finance to ship reliable, low-latency production systems and monitoring.

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.