Artificial Intelligence Jobs in Santa Clara, CA

Build and scale an enterprise Retrieval-Augmented Generation platform using Go and Python. Develop full-stack features, backend microservices, frontend interfaces, and AI/ML integrations in partnership with NVIDIA engineering teams. Own features from concept through production, while improving system performance, testing, security, availability, and reliability. Apply hands-on expertise in RAG, LLM pipelines, and AI-driven data retrieval architectures to deliver low-latency enterprise capabilities.

Leads technical architecture and solution design for complex cloud, AI, data center, hyperscale, and WAN networking sales opportunities. Translates customer requirements into scalable solutions, leads demos and proof-of-concepts, prepares RFP responses, and engages senior stakeholders. The role develops architecture playbooks, mentors sales engineering teams, influences deal strategy, and partners with customers, hyperscalers, service providers, and technology partners globally. Travel of up to 50%, including international travel, is required.

Leads a multi-year Agentic AI transformation of the Product Development Lifecycle across Product Management, Engineering, and QA. Responsibilities include assessing current processes, designing the future operating model, managing change and executive alignment, driving implementation, defining roadmaps and KPIs, and establishing governance. The role requires strategic thinking, executive influence, cross-functional leadership, project management, and knowledge of software engineering practices within a B2B technology environment.

Develop machine learning and generative AI methods to optimize chip layout for performance, power, and area. Automate physical design, verification, and exploratory PDK generation; build benchmarking, modeling, and QA systems; and evaluate advanced device architectures and process technologies for sub-2nm nodes. Collaborate with global R&D organizations and research consortia, transfer technology to Samsung’s Korea-based development teams, and guide cross-functional technical initiatives.

Build and maintain software that integrates ML/GenAI models with Kubernetes-based edge orchestration. Develop controllers, operators, CRDs, and Helm charts to schedule and deploy AI workloads at the edge, integrate ONNX/PyTorch/TensorFlow models, use AI coding agents, contribute to open-source components, and collaborate across AI, infrastructure, and product teams to ship features end-to-end.

Leads technical and writing teams delivering generative AI products, coordinating customers, data, product, and research teams. Tracks annotator metrics, manages labeling initiatives, creates prompts and responses, develops stakeholder guidelines, and trains teams on LLM and dataset best practices. The role includes customer interaction, quality assurance, and exposure to potentially toxic or sensitive content.

The Senior Manager of Embodied AI will oversee AI/ML engineering teams, manage operational excellence in data scaling for autonomous vehicles, and collaborate across teams to improve performance and innovation in AV technology.

Design and build AI agents, first-party web and mobile apps, and scalable backend services. Integrate LLM frameworks and enterprise systems, implement APIs and microservices, ensure observability and security, and collaborate with Finance, Security, and Engineering stakeholders.

Lead development of next-generation entity resolution solutions using transformers, graph ML, deep learning, and big-data tooling. Partner with engineers and product to design, train, evaluate, validate, and deploy ML models at scale; leverage Agentic AI workflows; translate technical work into business impact across enterprise use cases.

Lead and grow a technical team responsible for verifying AI compilers for next-generation workloads. Define formal verification requirements, drive AI-assisted and compiler-aware verification (fuzzing, differential testing, symbolic reasoning, formal methods), and partner with compiler, CUDA, runtime, and ML framework teams to build scalable verification infrastructure and improve production confidence.

Build and test Marvis Minis across embedded firmware and cloud services. Develop Minis features, author network/application validation tests, debug cross-layer issues spanning AP firmware to cloud data pipelines, extend platform to additional devices, and maintain cloud processing components (Storm, Airflow, Kafka, Redis, Elasticsearch). Collaborate with firmware, data science, QA, and operations and participate in production rollouts and incident response.

Lead US TMT business development with an AI focus: identify revenue opportunities from AI regulation, M&A, governance and data trends; develop client strategies, communications and targeted outreach; support pitches and major US TMT projects; coach and manage MBD colleagues; collaborate with partners and international teams to drive client relationships and revenue growth.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

The Technical Solutions Engineer resolves customer issues, troubleshoots complex network environments, and collaborates with engineering teams to ensure customer satisfaction.

The Solutions Architect will implement ERP projects focusing on NetSuite integration, optimize functionalities, and ensure system reliability while collaborating with stakeholders.

The engineer will leverage AI to enhance pre-silicon verification processes, collaborate with teams, and improve methodologies using advanced tools.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.

Design and deploy scalable AI inference architectures for strategic partners. Build and operate GPU inference recipes, optimize pipelines using leading inference backends, and guide disaggregated serving deployments. Evangelize Kubernetes, compute fabric, DevOps, and observability practices while mentoring customers and internal teams. Lead technical engagements, resolve complex infrastructure issues, create reference architectures, and provide product insights. The role also involves agent design, sandboxing, memory and retrieval systems, evaluation, skill governance, and model customization.

Lead AI workload characterization and storage architecture for large-scale inference, RAG, and training systems. Analyze performance across inference runtimes, Linux storage and networking, SSD hardware, and GPU infrastructure. Drive data-placement strategies, modeling, benchmarking, customer proofs of concept, standards engagement, cross-functional architecture decisions, and technical mentorship. The role focuses on integrating AI data paths with NAND, SSD, NVMe, and networked storage systems.

Build and optimize high-performance LLM inference systems across data centers and edge devices. Develop inference engines, accelerate models on GPUs and AI hardware, design Kubernetes-based autoscaling architectures, and implement observability, load testing, and reliability frameworks. Improve latency, throughput, cost efficiency, and scalability across real-time and batch AI workloads while collaborating with hardware and cross-functional engineering teams.

Define architecture and technical direction for AI decision systems, agentic AI platforms, MCP and A2A integrations, multi-agent workflows, and enterprise network validation automation. Lead scalable backend, API, database, Kubernetes, cloud, observability, and production architecture. Drive cross-functional technical alignment, establish engineering standards, influence platform strategy, and mentor senior engineers and technical leads. Design secure, reliable systems supporting orchestration, approval-gated actions, state management, retries, durable workflows, and distributed event-driven operations.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Apply deep battery-systems expertise to define SOC/SOH/thermal states and BMS logic, incorporate safety constraints, design agentic AI decision frameworks, analyze large-scale vehicle and telematics data with Python, and collaborate across BMS, data science, and AI teams to develop adaptive battery management strategies.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.