Artificial Intelligence Jobs in San Jose, CA

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.

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.

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.

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.

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.

Lead RTL development and microarchitecture of an AI-enabled RISC-V CPU core, including vector and matrix engines. Drive design for performance, power, area, verification support, and SoC integration while collaborating with cross-functional teams to meet timing, reliability, testability, and power goals.

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.

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

The role involves designing, building, and maintaining AI agents for Roku TV, managing their development lifecycle, and ensuring performance in production environments.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Lead software-hardware co-design to optimize LLM and agentic AI inference. Profile and optimize model execution for memory bottlenecks, influence memory and accelerator architecture, and develop/benchmark inference and serving solutions using PyTorch and vLLM while mentoring teams on best practices.

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.

Remotely operate and supervise robotic systems using teleoperation consoles, monitor live video and telemetry, perform real-time task adjustments, collect and annotate operational data, document issues, and provide feedback to engineering for continuous improvement.

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.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Senior technical leader responsible for architecting production-grade AI infrastructure for media & entertainment partners. Translate business needs into scalable GPU, storage, networking and inference solutions; validate ISV integrations; influence product roadmap; engage CTO-level stakeholders; and grow a team as the vertical scales.

Lead the strategy for AI analytics products, building AI-powered workflows for performance diagnostics and insights. Collaborate with teams to enhance data-driven decision-making and product health monitoring.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Lead product strategy and roadmaps for post-training and RL workflows on NVIDIA GPUs/CPUs. Collaborate with customers and internal teams to build scalable distributed frameworks, optimize training/inference pipelines, and drive go-to-market efforts for OSS and developer-focused products.