Artificial Intelligence Jobs in Santa Clara, CA

Lead research on generative world models and synthetic data to improve training, evaluation, and generalization of end-to-end autonomous driving systems. Prototype and develop algorithms, collaborate with research and product teams to scale advances into production, define research direction and roadmaps, and drive technology transfer for safe Level 4 autonomy.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Design, implement, and optimize AI algorithms and software for computer vision, NLP, recommendation systems and ASR. Develop, test, and maintain models and production-quality code, participate in code reviews and CI/CD workflows, and document algorithms and technical processes.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Lead design, training, and deployment of small, efficient language models and agentic systems for security: threat detection, policy enforcement, real-time inference, edge deployment, adversarial robustness, and productionization in security pipelines.

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

Lead product strategy, roadmap, and go-to-market for NVIDIA enterprise game development solutions. Partner with engineering, ISVs, OEMs, and sales to integrate RTX, generative AI, and developer workflows into studio pipelines. Drive positioning, demos, trade-show strategy, sales enablement, and lighthouse case studies to grow adoption among game developers and publishers.

Build scalable, resilient agentic AI and LLM/ML solutions. Translate requirements into high-level and detailed architectures. Implement solutions using Python/C++ and ML frameworks, applying AI/ML fundamentals, data structures, algorithms, and distributed systems.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

As a Senior AI Systems Performance Engineer, you will benchmark AI workloads, optimize Ethernet fabric performance, and troubleshoot system bottlenecks, focusing on delivering actionable insights across teams.

Design and scale the infrastructure for Fury's AI model training and deployment, ensuring efficient data pipelines and collaboration with AI teams.

Lead technical and operational programs for enterprise AI partners. Define schedules, translate customer requirements, drive cross-functional execution from design to production, manage risks and post-deployment support, and coordinate global teams to integrate complex AI model stacks into customer environments.

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.

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal teams.

The internship involves multiscale modeling focusing on physics and AI modeling. Interns will run simulations and define workflows using Python and related libraries.

Lead technical product marketing for AI compute, owning positioning, competitive strategy, and technical content. Translate systems-level innovations into customer and executive narratives, drive competitive intelligence, support launches, and lead a technical product marketing team aligned with product, engineering, and sales.

Design and implement onboard perception systems (computer vision, localization, visual-motor policies, and data annotation), own end-to-end product lifecycle, deliver production-grade software, and collaborate cross-functionally to optimize the autonomy stack for humanoid robots.

The role involves owning the infrastructure for training and deploying RL-based control systems in humanoid robots, enhancing speed, efficiency, and collaboration among teams.

Lead end-to-end product management for a multi-cloud AI platform (Generative and Agentic AI). Define vision, roadmap, and features; collaborate with engineering, UX, and stakeholders; prioritize work in agile sprints; ensure platform scalability, quality, and developer experience; build and lead a small PM team; and evangelize platform adoption across the enterprise.