Artificial Intelligence Jobs in San Jose, CA

Develop and optimize software for IP customers, including firmware, drivers, and APIs. Collaborate with cross-functional teams for software integration and troubleshooting.

The AI Research Engineer designs and optimizes AI systems for enterprise automation, collaborating across teams to improve efficiency and decision-making.

Responsible for architecting AIOS solutions, leading proof-of-concepts, collaborating with engineering teams, defining roadmaps, and delivering project requirements.

Design and implement high-performance, scalable inference systems for LLMs and VLMs across cloud, edge, and hybrid platforms. Develop and optimize custom kernels for hardware accelerators (GPU/NPU/DSP), integrate tensor/model parallelism, caching, and quantization, and collaborate with system and hardware teams to deploy production-grade AI inference.

The role involves researching AI system architecture, developing performance models, analyzing AI workloads, and guiding design decisions while collaborating across teams.

Seeking a Principal AI System Architect to lead research and design next-gen AI system architectures, optimizing performance for AI workloads with a focus on memory capacity and communication.

The role involves researching AI system architecture, developing performance models, making design decisions, and leading architecture strategies. It requires collaboration across teams to evaluate AI workloads and scalability issues.

The role involves architecting and leading the development of a neuromorphic AI execution stack, focusing on firmware, runtime, and performance infrastructure while collaborating with multiple teams for integration and optimization.

Design, implement, and optimize Intel's neuromorphic AI compiler and runtime. Build tooling for performance analysis, verification, debugging, and optimization. Integrate the software stack with robotics and edge AI ecosystems. Collaborate with hardware, compiler, and systems teams and external customers to deliver production-grade, high-performance software.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

The role involves deploying AI networking solutions in data centers, driving architectural decisions, troubleshooting network issues, and collaborating on customer projects for NVIDIA's technology solutions.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

Contract SRE to build, operate, and automate colo, on‑prem GPU clusters, and cloud infrastructure. Own provisioning, IaC (Terraform/Ansible), observability (Prometheus/Grafana or DataDog), incident response, runbooks, and customer-facing platform reliability.

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.

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

Lead discovery, prototype, and productize ML/AI and edge computing innovations. Build proofs of concept, collaborate with cross-functional teams, engage customers/partners for co-innovation, influence product roadmaps, and evangelize solutions to drive commercialization and market adoption.

Own and optimize the AI inference and execution layer: quantization, KV-cache, batching, latency/memory tuning, fine-tune and evaluate models, build bounded task runtimes, enable hardware acceleration and partner with systems/backend teams to ship production-grade agentic AI capabilities.

Design and deploy rack-scale AI infrastructure and cluster solutions for NVIDIA products. Collaborate with product teams and cross-functional engineering groups to onboard hardware/software stacks, optimize cluster deployments, and support multi-site datacenter and lab rollouts. Troubleshoot complex system failures, automate provisioning, and improve time-to-market for next-generation GPU/Tegra-based products.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

Define and execute the scientific roadmap for search and recommendation: architect large-scale ranking and retrieval systems, develop and distill Ranking Foundation Models, integrate vector/RAG/hybrid retrieval into production, partner with product and engineering, and lead and scale multidisciplinary ML teams to optimize marketplace health.

Lead global strategic partnerships across cloud, GPU, and hardware providers. Identify and negotiate commercial agreements, manage key partner relationships, align cross-functional stakeholders, build business cases, and support executive strategy to enable Higgsfield's AI infrastructure scale and growth.

Bring up and optimize foundation models (LLMs and multimodal) on the SambaNova platform. Profile and analyze execution to find bottlenecks across model, compiler, runtime, and hardware layers. Implement optimizations for throughput, latency, memory, and scalability and build tools and benchmarks for large-scale inference.

The Senior Manager, PR will lead communications strategies to enhance awareness of AI applications across various industries, collaborate with leaders, and manage media relations for impactful coverage.

Drive the development of AI kernel generation products, working with internal and external teams to optimize workflows and enhance performance of AI deployments.

Own and scale the Nx-based monorepo, build system, and CI/CD to make builds, tests, and developer workflows fast, reliable, and observable. Build developer tooling, local dev stacks, and production-grade developer infra with SLOs, on-call, and measurable DX improvements.