Artificial Intelligence Jobs in Santa Clara, CA

Lead product strategy, roadmaps, and go-to-market for power semiconductor solutions targeting AI datacenter infrastructure and advanced power conversion. Drive market analysis, customer engagement, business planning, cross-functional commercialization, and thought leadership in SiC/GaN and next-generation power delivery architectures.

Develop physics-aware AI models and optimization workflows for semiconductor engineering. Design and train surrogate, operator-learning, and foundation models, build scalable data generation/training/deployment pipelines, collaborate with domain experts, and publish and drive adoption of Scientific AI solutions.

Own the inference performance product roadmap across the full stack, turning advanced LLM optimization techniques into widely adoptable platforms. Define strategies for agentic and multi-turn workloads, partner with open-source and internal teams to land optimizations, and own benchmarking, release readiness, regression tracking, and production feedback loops to improve latency, efficiency, and cost per token.

Lead technical strategy and execution for Merlin's AI Core platform across perception, sensor fusion, planning, and control. Build and manage a multidisciplinary engineering organization, own architecture from data pipelines to real-time onboard inference, enforce aviation-grade safety and quality, partner with certification and systems teams, recruit senior talent, and translate research into product opportunities.

Optimize large-scale distributed ML training and petabyte-scale offline inference for throughput and cost. Profile across stack, close performance gaps, improve multi-node scaling and cluster goodput, and build benchmarking and observability tooling while collaborating across teams.

Design and build agentic AI systems and LLM-based solutions for cybersecurity platforms. Implement ML/DL pipelines for threat detection, fine-tune and optimize models, create evaluation benchmarks, collaborate with security researchers and engineers, and deploy production-ready AI-driven SOC, SOAR, and SIEM workflows.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Founding research hire to develop clinical reasoning and computer-use models using proprietary hospital and clinic data. Own end-to-end training and evaluation pipelines, design benchmarks exposing LLM weaknesses on clinical reasoning, run experiments, and publish technical reports and papers. Collaborate closely with founders and deploy research into products that improve physician workflows.

Lead and govern an integrated AI-enabled Quote-to-Cash transformation portfolio: maintain roadmap, manage schedule, risks, dependencies, budgets and KPIs; produce executive reporting and dashboards; coordinate cross-functional teams from ideation to scaled deployment; drive PMO cadence, governance, and continuous improvement while mentoring workstream leads.

Lead AI adoption and enablement for Quote-to-Cash transformations by creating adoption roadmaps, change management plans, stakeholder engagement, communications, role-based training, champion networks, and adoption measurement. Partner with business, product, UX, and technology teams to embed adoption through ideation, pilots, launch, and scale, identify and mitigate adoption risks, and report adoption outcomes to leadership.

Design, fine-tune, and deploy multimodal vision-language and VLA models for autonomous eVTOL aircraft; benchmark, validate in simulation/hardware/flight, and productionize models for edge deployment under safety and performance constraints.

Lead the architectural design for AI hybrid inference systems, mentor team, and guide development of high-availability orchestration engines while driving innovation.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Drive adoption of AI inference platform across large enterprises, own sales cycle, lead technical evaluations, engage executive stakeholders, and shape GTM strategy.

The Senior Staff AI Engineer will lead the design and optimization of reinforcement learning systems for enterprise applications, focusing on scalable architectures and production deployment. Responsibilities include mentoring, collaboration, and ensuring compliance with AI ethics and safety standards.

As a Senior AI Engineer, you will architect AI systems for marketing, build generative pipelines, and ensure compliance with privacy standards, all while leading MLOps processes.

Lead research and development of novel ML and deep learning algorithms from data and architecture design through evaluation and transition to production. Prototype research ideas, run benchmarking experiments, build reproducible research tooling, and communicate findings to leadership.

Lead enterprise sales and go-to-market for NVIDIA AI and accelerated computing with Lenovo: develop technical understanding, package joint solutions, co-market/co-sell across channels, build ecosystem partnerships, and drive integration, commercialization, and thought leadership.

Lead performance engineering for AV and robotics workloads: engage customers, develop parallel algorithms and GPU-optimized libraries, profile and tune GPU kernels, optimize distributed transformer communication using NCCL/NVSHMEM and interconnect strategies, and collaborate with architecture and system teams to influence next-generation hardware and software.

The role involves developing AI applications for VLSI design using machine learning and related technologies. Duties include infrastructure design, AI solution implementation, collaboration with teams, and maintaining design databases.

Build and embed LLM-agent workflows into silicon engineering flows (RTL, verification, DFT, physical design, validation). Integrate agents with simulators, regressions, lab equipment, and CI; design rigorous evals, drive adoption, and accelerate chip development through measurable automation.

Manage assigned accounts as primary customer contact, develop and execute sales strategies, drive design-wins and new business, negotiate contracts, collaborate with FAEs and product teams, maintain executive-to-working-level relationships, manage complex sales cycles, CRM/funnel forecasting, and travel as needed.

Design, prototype, and ship production-grade AI solutions (LLMs, agents, RAG) integrated with enterprise systems. Define success metrics, build evaluation harnesses, and harden systems for scale, observability, and cost efficiency. Collaborate with product, design, and business stakeholders and contribute reusable tooling and components.

Design and deliver enterprise-grade AI/GenAI solutions on Salesforce or ServiceNow. Lead RAG pipelines, LLM workflows, NLP automation, and vector retrieval integrations. Architect scalable API/microservice integrations, ensure performance, security, and reliability, and define AI governance and guardrails. Collaborate with product, engineering, and customers through discovery, workshops, and demos to operationalize AI for Field and Customer Service workflows.