Artificial Intelligence Jobs in Sunnyvale, CA

Leads the productization, deployment, and scaling of perception and physical AI software for robotics and automation. Owns software strategy, architecture, SDK and platform development, field deployment, OTA updates, reliability, and operational excellence. Builds and scales engineering teams across perception, autonomy, infrastructure, and platform functions while partnering with algorithms, hardware, product, systems, applications, and customer teams. Represents software strategy to executives and the board.

Own Lyte’s marketing campaigns and content strategy across social media, blogs, newsletters, press outreach, and thought leadership. Translate complex robotics and Physical AI technologies into clear messaging, support product launches and go-to-market initiatives, manage content calendars, collaborate cross-functionally, and measure campaign performance. The role requires strong writing, technical storytelling, social media expertise, and comfort working autonomously in a fast-moving startup environment.

Lead product marketing for AI infrastructure: analyze market and competitive signals, develop positioning and messaging, create content (blogs, webinars, tutorials), support product launches, and partner cross-functionally with engineering, sales, and GTM teams to showcase NVIDIA platform value.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

Analyze, profile, and optimize AI training workloads on NVIDIA GPUs across the hardware/software stack. Implement production-quality software, build MLPerf training submissions, model workloads in simulators, and develop automation tools to identify and resolve performance bottlenecks.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Research, prototype, and implement methods to improve efficiency and deployability of LLMs, VLMs, multimodal models and agents. Focus on compression, quantization, inference and reasoning optimizations, novel architectures (MoE, SSMs, Looped Transformers), gradient-free methods, and inference-time optimizations. Build evaluation pipelines, run benchmarks, and contribute to publications, open-source, and IP.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

Design and deliver high-performance, area- and power-efficient ASIC/GPU micro-architecture and RTL. Develop agentic AI solutions to craft micro-architecture, implement RTL, and produce fully verified, synthesis/timing-clean designs. Collaborate with designers and tool owners, and build LLM-powered engineering assistants, multi-agent systems, and multi-modal dialogue tools to accelerate chip design workflows.

Design AI/BI product experiences that make natural-language data queries and AI-generated insights intuitive, transparent, and actionable. Lead end-to-end design from exploration to validation and action, collaborate with product and engineering, define AI-native enterprise patterns, and influence long-term AI/BI strategy.

The Head of AI Applications will define and execute the AI strategy within SiTime, leading the development and deployment of AI solutions across various business functions while driving adoption and managing a high-performing team.

The Staff AI Product Designer will lead the design process for AI products, collaborating with diverse teams to create user-centered solutions and drive product strategies while ensuring ethical design practices.

Lead technical engagements with robotics customers to design and integrate voice AI into mobile robots, own POCs and technical evaluations, clear deployment roadblocks, collaborate with sales/product/services, and feed customer learnings into the product. Travel across Europe for onsite evaluations (20–30%).

Enable end-to-end agentic AI GPU deployments on NVIDIA RTX and DGX by profiling and optimizing performance, contributing to open-source LLM/GenAI projects, delivering trainings and sample code, collaborating with drivers/architecture teams, and mentoring junior engineers.

Design, build, and operate large-scale ML training and evaluation infrastructure for RL/AI research. Enable distributed, cost-aware GPU training, benchmarking, data curation, and continuous evaluation; collaborate with research and platform teams to productionize models.

Build and productionize scalable backend services, APIs, and AI workflows to connect requirements, modeling, validation, and test data for autonomous vehicle programs. Implement traceability, versioning, dashboards, and KPIs, refactor prototypes into certifiable systems, and collaborate with systems engineering, safety, and autonomy teams to support compliance and validation at scale.

Build and ship shared AI infrastructure, agent frameworks, and tooling; partner with product teams to productionize LLM and agentic features; own GenAI best practices for architecture, safety, evaluation, and deployment; develop agentic systems and AI SRE tooling; engage with customers to shape roadmap and demos.

Lead and manage a team delivering advanced AI/HPC and data center infrastructure solutions. Oversee planning, budgeting, solution design, pre-sales technical support, vendor enablement, reference architectures, KPI reporting, documentation, and cross-functional collaboration to ensure successful deployment of GPU‑accelerated compute, networking, storage, and facility solutions.

As an Engagement Manager, you will manage client relationships, lead AI consulting projects, and support business development efforts, all while enhancing enterprise AI adoption.

Short-term intensive research internship assisting in dataset collection and preprocessing, model architecture design and tuning, implementation of testing frameworks, analysis of model accuracy and bias, algorithm refinement, and documentation to support foundation-model research.

Short-term, intensive internship supporting foundation model work: prepare datasets, assist with model architecture and testing, analyze outputs for accuracy and bias, refine algorithms, and document findings for research collaboration.

As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.