Artificial Intelligence Jobs in Sunnyvale, CA

Source and screen passive technical talent across AI, machine learning, robotics, computer vision, SoC, digital health, and data science roles, including Director-level executives. Develop multichannel sourcing strategies, analyze market data, advise recruiters and hiring managers, build diverse talent networks, promote employer branding, and track recruiting conversion metrics.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

Lead design and delivery of production-grade AI capabilities to improve developer productivity across engineering tools, CI/CD, and embedded software workflows. Define technical vision, integrate AI into toolchains, drive productionization, establish responsible AI standards, mentor engineers, and influence cross-functional roadmaps and implementations.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Build and ship scalable, performant AI-powered Copilot web experiences for millions of users. Design reusable frontend systems, deploy frontend infrastructure, and collaborate with AI researchers, product managers, and designers. The role emphasizes user-centric product development, safety, reliability, rapid iteration, and high-quality execution in a collaborative environment.

Contribute to power estimation models for GPUs, explore architecture for energy efficiency, analyze performance versus power, and employ machine learning for modeling power and performance metrics.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.

Leads product positioning, messaging, go-to-market strategy, launches, segment campaigns, technical content, competitive differentiation, and global sales enablement for FlashBlade//EXA scalable AI and HPC storage solutions. Partners with Product Management, Sales, and Corporate Marketing to drive adoption, revenue growth, and market leadership across enterprise, life sciences, financial services, and academic customers.

Designs, develops, and deploys enterprise Generative AI platforms using LLMs, RAG, agentic architectures, and modern orchestration frameworks. Builds Python microservices and AI workflows across Azure and AWS, while implementing GenAIOps, containerization, infrastructure as code, CI/CD, monitoring, security, governance, and compliance. Partners with engineering, platform, architecture, security, and product teams to deliver scalable, resilient, production-ready AI solutions.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Lead strategy and roadmap for AI-powered personalization and content ranking across Edge, Windows, Copilot, and partners. Define representation, retrieval, features, objectives, training, evaluation, and serving for recommendation and personalization systems. Drive evaluation, experimentation, and cross-team integration; set quality bars, privacy and policy guardrails; align VP-level stakeholders and influence resource allocation to build scalable personalization and ranking platform capabilities.

Lead development of scalable stress, validation, and performance tooling for MAIA AI accelerator platforms. Build workload generation, kernel optimization, compiler/SDK integration, automated orchestration and telemetry, platform validation suites, benchmarking, and CI-driven regression infrastructure to enable platform bring-up, reliability, and performance optimization across hardware and software stacks.

Lead competitive strategy for Microsoft's AI and cloud infrastructure products, evaluating accelerators, CPUs, memory, networking, storage, and rack-scale systems. Analyze technology roadmaps, performance, power, cost, supply, utilization, and total cost of ownership. Partner with architecture, engineering, finance, sourcing, and product teams to develop data-driven recommendations, competitive assessments, dashboards, and executive-level insights influencing Azure product roadmaps and investment decisions.

Define long-term architecture and technical strategy for Target's enterprise ML/Ops platform. Design cloud-native, scalable, secure Kubernetes-based platforms; establish ML lifecycle, deployment, governance, observability, and responsible AI standards; evaluate technologies; mentor engineers; and partner with infrastructure, security, and product teams to modernize platform capabilities.

Owns AI-driven product design from ambiguous problem framing through production-quality prototypes and shipped experiences. Extends design systems, contributes directly to front-end implementation using AI development tools, evaluates and refines AI-generated work, collaborates with product and engineering, and mentors designers. The role requires startup experience, zero-to-one product delivery, strong design fundamentals, Git and GitHub fluency, prompt scaffolding, and autonomous execution.

Design, deploy, and operate distributed backend systems supporting large-scale, low-latency machine learning inference. Lead inference platform direction, optimize high-throughput serving infrastructure, collaborate with ML engineers, and ensure production reliability through monitoring and observability. Manage GCP cloud infrastructure and Kubernetes workloads while applying DevOps, SRE, testing, deployment, and security best practices.